{"as_of":"2026-08-21T01:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:568d8c267043ad5f7c69d24df39c9aa4eb374b13f9b0cee488e6f28712891ba0","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:37:01.189240Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-18T15:24:04.079011Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-18T15:26:33.774099Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"cited_work":{"arxiv_id":"2507.15601","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.15601","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Optimal batch-size control for low-latency federated learning with device heterogeneity","venue":null,"work_id":"df56623d-0734-4644-a4d7-2f60fa1ed9aa","year":2025},"citing_paper":{"arxiv_id":"2509.17398","last_updated":"2026-04-08T13:39:21Z","snapshot_observed_at":"2026-08-16T17:40:17.466000Z","submitted_at":"2025-09-22T06:57:46Z","title":"Optimizing Split Federated Learning with Unstable Client Participation","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-18T15:24:04.079011Z"},"links":{"cited_paper":"/paper/2507.15601","citing_paper":"/paper/2509.17398"},"observation_digest":"sha256:48824d24d5e85dffb8bf4fad1af14155e58cd96a7fdc251ff94c73f6a61f1b9e","observation_id":"b85938c2-440d-4ebc-a1dd-467dd5a1bb8e","resolution":{"observed_at":"2026-05-18T15:26:33.777262Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.15601/citation-record","integrity":"/paper/2507.15601/integrity","json":"/paper/2507.15601/citation-record.json","paper":"/paper/2507.15601"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.383376Z","title":"A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,","venue":null,"work_id":"2c8550d6-43e2-487f-ad90-447f4a33842e","year":2019},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.914700Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:f60785ec32a83ae008adeb71c64eea54d560a0eb87576af4bfb4072668c45344","observation_id":"f8dcb94b-6176-49e4-8fd5-1b38cab95f8a","resolution":{"observed_at":"2026-08-06T15:37:02.387683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.06726","last_updated":"2025-07-24T08:54:25Z","snapshot_observed_at":"2026-08-16T12:59:09.565955Z","submitted_at":"2025-01-12T06:25:58Z","title":"Integrated Sensing and Edge AI: Realizing Intelligent Perception in 6G","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.06726","snapshot_observed_at":"2026-08-06T15:37:00.920375Z","title":"Integrated sensing and edge AI: Realizing intelligent perception in 6G,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.920375Z"},"links":{"cited_paper":"/paper/2501.06726","citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:ed51b0483164bcb87769bf9335fc4e614bd191a10d2ec89427b5b867bd44f676","observation_id":"9cabefea-bf88-48c6-af11-107dce834990","resolution":{"observed_at":"2026-08-06T15:37:00.920375Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.368082Z","title":"Space–ground fluid AI for 6G edge intelligence,","venue":null,"work_id":"ba109151-8c69-4f51-9fb9-51b1cecbc924","year":null},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.929395Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:bc567c11e76273510b6bcd3e86dd531120c8a8c139746bf4ba87d16b55d04569","observation_id":"498a4135-4849-4737-9270-f9c32238130e","resolution":{"observed_at":"2026-08-06T15:37:02.372930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.351820Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":"9d6aeb54-263c-4d91-98b2-612b6f94c450","year":2017},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.935843Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:b0d9341cc0830f402705907ac4561d33a3df38c9809225602b9a5115e71baa0b","observation_id":"c8cccb3b-c307-4cea-97aa-0c8081ea06a7","resolution":{"observed_at":"2026-08-06T15:37:02.356360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:00.941941Z","title":"Federated machine learning: Concept and applications,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.941941Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:a00321f78e88df66c5d1569cb1f75ac768164ccb0fe838a9e080c784e0d797fc","observation_id":"7a2debfd-8e32-42f9-8603-eb0251082c89","resolution":{"observed_at":"2026-08-06T15:37:00.941941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.325685Z","title":"Advances and open problems in federated learning,","venue":null,"work_id":"40af510e-7280-4ad0-9b30-8bdbe8c2d973","year":2021},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.949478Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:21a0ca4863ba4ed1b3a2c23e76c87ff7b356b207ee3266fd9e27cc60f51254f7","observation_id":"353a3a87-7351-419c-99db-ee68d50fd2dd","resolution":{"observed_at":"2026-08-06T15:37:02.330975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.309215Z","title":"Fedhome: Cloud-edge based personalized federated learning for in-home health monitoring,","venue":null,"work_id":"5f1173b7-61b4-4294-99ce-beff01ad8684","year":2020},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.955106Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:bd1057884be7fccf22ec520027a85703580349a33f0f458f81203e378b0ed715","observation_id":"7804f621-a3b6-4113-b99c-b9a0683be050","resolution":{"observed_at":"2026-08-06T15:37:02.314298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.294145Z","title":"GeFL: Gradient encryption- aided privacy preserved federated learning for autonomous vehicles,","venue":null,"work_id":"6eace642-63cd-4b93-8653-d4464beae41d","year":2023},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.962686Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:628e6f3af808013c68edea2c01dd28811e3096ab2dc3b3799b270e6183122c8b","observation_id":"ee05dba4-ee19-4c47-9041-90ba5f0527c0","resolution":{"observed_at":"2026-08-06T15:37:02.298949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.05492","last_updated":"2017-10-30T20:52:14Z","snapshot_observed_at":"2026-08-13T17:19:46.488095Z","submitted_at":"2016-10-18T09:11:51Z","title":"Federated Learning: Strategies for Improving Communication Efficiency","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.05492","snapshot_observed_at":"2026-08-06T15:37:00.967659Z","title":"Federated learning: Strategies for improving communication efficiency,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.967659Z"},"links":{"cited_paper":"/paper/1610.05492","citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:bcc939c1bbfdf554ac559bfd31639cf7b6c4843f5d780b82ba40c04e47fe95f9","observation_id":"42002947-c4fd-49ce-bd82-6a2b99bbf5a1","resolution":{"observed_at":"2026-08-06T15:37:00.967659Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.279195Z","title":"Model pruning enables efficient federated learning on edge devices,","venue":null,"work_id":"3835c0dd-2b94-48b2-8181-7a4463f724d2","year":2022},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.972864Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:d35d8de2ab7fa277157d6d8f4f7ab3cf4a36cc194109f384d2cfef109b915b0d","observation_id":"7316513a-b46a-4541-928c-93c17e83b864","resolution":{"observed_at":"2026-08-06T15:37:02.283901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.264199Z","title":"Deploying federated learning in large-scale cellular networks: Spatial convergence analysis,","venue":null,"work_id":"bec6595f-2728-477c-8cf9-e6a66e9f9cd1","year":2021},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.978036Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:42eeb472db0bb819e11cc4427afad47643ec7b526fd6377007c0e38feca8f8b7","observation_id":"23ca3f55-0c6e-4fe5-b358-5d3372cf3a4e","resolution":{"observed_at":"2026-08-06T15:37:02.268906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.250300Z","title":"UVeQFed: Universal vector quantization for federated learning,","venue":null,"work_id":"c6ceda03-7bdb-4dc7-83f1-7f8d96a35232","year":2020},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.983173Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:e7a2c21dce724b3cac0dd09db2c1339eeec62cf8d24d7195395696540df5a760","observation_id":"14f4e6db-155d-4301-bfea-de9e28571a0c","resolution":{"observed_at":"2026-08-06T15:37:02.254445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.12416","last_updated":"2021-04-26T09:13:31Z","snapshot_observed_at":"2026-08-16T18:29:07.993599Z","submitted_at":"2021-04-26T09:13:31Z","title":"Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression","version":1},"cited_work":{"arxiv_id":"2104.12416","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.12416","snapshot_observed_at":"2026-08-06T15:37:01.635297Z","title":"Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression","venue":"cs.LG","work_id":"265a24d6-6260-43af-83a4-e3ae9bed1d95","year":2021},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.987955Z"},"links":{"cited_paper":"/paper/2104.12416","citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:6fb835d8f87553515ca0a949ace3882b45986d1b5fba02c175efb4995ca0a27d","observation_id":"643f8d89-45e3-469a-a11b-4bac4179648c","resolution":{"observed_at":"2026-08-06T15:37:01.641832Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:00.994277Z","title":"Splitfed: When federated learning meets split learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.994277Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:5600344e1c324fd1fa380fe3d62154160060ddfc98683b029c660ce8a019bd0a","observation_id":"b87be946-3699-41e0-88a2-3bf53d95cca4","resolution":{"observed_at":"2026-08-06T15:37:00.994277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.224938Z","title":"Broadband analog aggregation for low-latency federated edge learning,","venue":null,"work_id":"c41310df-7c9b-4712-b15a-c3b798a1e518","year":2019},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:00.999196Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:489478ba6f60b54dafc9d25f30d692e01f265f66494d17b4e7c3c0d39ef16672","observation_id":"520cbb0d-d066-4cfe-92ba-88feda161fee","resolution":{"observed_at":"2026-08-06T15:37:02.229138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.210110Z","title":"Federated learning via over- the-air computation,","venue":null,"work_id":"4cf9ef6d-64ed-4ca3-8d03-aa5d8871e73c","year":2022},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.007857Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:49210d7347e33335c4e19fae3ecb6c80bf98463beffb84082bf9562ee1aa746a","observation_id":"256c6687-38c7-49d3-85ff-02037b01ba0d","resolution":{"observed_at":"2026-08-06T15:37:02.215352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.194958Z","title":"Spectrum breathing: Protecting over-the-air federated learning against interference,","venue":null,"work_id":"9e2c9ffe-d5e4-483a-acd4-b2a55e3ec9b5","year":2024},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.013355Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:99a304260ad427b8148a7efda0ca5deba41464f0512c1dcc4d9ca49497bf1dd8","observation_id":"b56b1464-ce1a-4d39-9158-79d24ec68518","resolution":{"observed_at":"2026-08-06T15:37:02.199963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.018242Z","title":"Airbreath sensing: Protecting over-the-air distributed sensing against interference,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.018242Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:bcd6d11a4a9854dccaa8fb63c209a462d0107a00ddb18103a51d32373ea5f52e","observation_id":"ddf4775f-0d03-48ee-a573-a9581e925782","resolution":{"observed_at":"2026-08-06T15:37:01.018242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.178734Z","title":"Federated learning over wireless fading channels,","venue":null,"work_id":"b8f6a442-ecbc-42b6-b0ca-c29bd96627d7","year":2020},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.024023Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:fbb791a61aadf254c28cab6ef67266adb3d0a90718435f0c349adc9fdde38d31","observation_id":"8d02eb3f-1305-4fc7-b705-7603c7ac9d1c","resolution":{"observed_at":"2026-08-06T15:37:02.184339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.02677","last_updated":"2018-04-30T21:53:41Z","snapshot_observed_at":"2026-08-09T05:23:26.365677Z","submitted_at":"2017-06-08T16:51:53Z","title":"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.02677","snapshot_observed_at":"2026-08-06T15:37:01.029636Z","title":"Accurate, large minibatch SGD: Training ImageNet in 1 hour,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.029636Z"},"links":{"cited_paper":"/paper/1706.02677","citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:9889996e4b60bb5f03608890d7646c7203020f090668633d034b4037ef5cf9f9","observation_id":"13cba22f-359a-4214-8435-9befa4382cb2","resolution":{"observed_at":"2026-08-06T15:37:01.029636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.161207Z","title":"To talk or to work: Dynamic batch sizes assisted time efficient federated learning over future mobile edge devices,","venue":null,"work_id":"63385f90-2a5a-4e4c-bb36-0cc4681fee5a","year":2022},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.035876Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:f353fd98f0b93082332d837ba2a187a77480f882956ec09d5afb64518ad97ce6","observation_id":"0b646557-2f4e-46f5-85b6-35e92d26514d","resolution":{"observed_at":"2026-08-06T15:37:02.166172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.143783Z","title":"ARM Cortex-M7 Processor Datasheet,","venue":null,"work_id":"e53908cc-0e8c-46e6-ab8a-d6b2ea1a9282","year":2023},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.041445Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:287ab8b502a074c29b8d98fb712dc172db2670604d1d303e4932d070ed68e0b8","observation_id":"95f586ac-404e-4741-8d95-46c4d8caa8d9","resolution":{"observed_at":"2026-08-06T15:37:02.149470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.125589Z","title":"Apple A18 Pro Chip Specifications,","venue":null,"work_id":"86f4d7e5-6041-4dcc-83d4-171a5633c58d","year":2024},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.046432Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:a21824d55c759acda2187aa69b656d439b5e39ce1d30f04d1981e7cc82701000","observation_id":"efec4618-3dd3-49c5-a1c1-ef2d0cffb553","resolution":{"observed_at":"2026-08-06T15:37:02.131528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.03934","last_updated":"2020-12-05T01:33:57Z","snapshot_observed_at":"2026-08-19T20:51:27.056831Z","submitted_at":"2019-03-10T06:19:38Z","title":"Asynchronous Federated Optimization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.03934","snapshot_observed_at":"2026-08-06T15:37:01.051178Z","title":"Asynchronous federated optimization,","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.051178Z"},"links":{"cited_paper":"/paper/1903.03934","citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:9fc817751cc60388c1dda9096cc1fe7a8de09dfb06ac86d200e83b7358c2ce53","observation_id":"0ca72c45-a87b-4f0e-8314-4d845f85de04","resolution":{"observed_at":"2026-08-06T15:37:01.051178Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.095840Z","title":"Asynchronous federated learning over wireless communication networks,","venue":null,"work_id":"288173da-9057-40e9-b8bd-6d538880fa37","year":2022},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.057160Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:d1ac5fe7cec759ae0f80d53a1483b21b950a574a0ea40346d78ef7d14a76ddc9","observation_id":"a3aa833a-7a25-4f85-aed1-0d3a3890abb5","resolution":{"observed_at":"2026-08-06T15:37:02.103001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.076530Z","title":"Asynchronous federated learning on heterogeneous devices: A survey,","venue":null,"work_id":"ebc13f2b-661a-45a4-b1c9-f74a5ae6f14e","year":2023},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.061927Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:2b3e046c25d012260ab81ceab07755f2243a5a8d725e0718a004329060f0cc4b","observation_id":"26ca3aca-d19a-4896-9643-96a6009e38bc","resolution":{"observed_at":"2026-08-06T15:37:02.083582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1604.00981","last_updated":"2017-03-21T07:44:39Z","snapshot_observed_at":"2026-08-14T22:03:08.190474Z","submitted_at":"2016-04-04T18:40:05Z","title":"Revisiting Distributed Synchronous SGD","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1604.00981","snapshot_observed_at":"2026-08-06T15:37:01.067056Z","title":"Revisiting distributed synchronous SGD,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.067056Z"},"links":{"cited_paper":"/paper/1604.00981","citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:456aec2fe9857c0736e9200e2ade5890db0c0a9e24e622d253a363cc2ed5bc69","observation_id":"334d32d9-8901-44ac-967a-5e338c80dbe9","resolution":{"observed_at":"2026-08-06T15:37:01.067056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.056193Z","title":"Bandwidth allocation for multiple federated learning services in wireless edge networks,","venue":null,"work_id":"684a0a63-365f-4374-9690-c13156a17cbf","year":2021},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.073703Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:b9a902ef74bc0ef8002b1a001ed7a9f64a351afec6527b9165485f0a62b27a3d","observation_id":"80f15f1f-8c58-4b4c-9879-a8db5137a6ee","resolution":{"observed_at":"2026-08-06T15:37:02.061378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.038635Z","title":"Client selection and bandwidth allocation in wireless federated learning networks: A long-term perspective,","venue":null,"work_id":"2ffc804e-4957-4339-b31e-f82b6c34719d","year":2020},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.079851Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:f33e38216a4c703c39f9cc18f784020b7a672f1f1eb10a553307198393ab5aef","observation_id":"234950dc-ef25-45dc-9ef0-a160114cc7a7","resolution":{"observed_at":"2026-08-06T15:37:02.044580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.021121Z","title":"Joint device schedul- ing and resource allocation for latency constrained wireless federated learning,","venue":null,"work_id":"c24066d0-3cab-4387-acdf-7b732d9c9b3d","year":2020},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.084400Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:01b89b5b2bb8fce14009143200cfff180218d2a73f312de95d643656b48cb556","observation_id":"37dbb8db-67f1-4711-8ac4-9d6466e15797","resolution":{"observed_at":"2026-08-06T15:37:02.026358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:02.000242Z","title":"Wirelessly powered federated edge learning: Optimal tradeoffs between convergence and power transfer,","venue":null,"work_id":"6fac2851-d5ec-4fd2-b89f-390b2bee3aaa","year":2021},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.090296Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:b0f1fe9984f95071739e509809897ed78456cade8dcd773386b9f0ea0955c3a5","observation_id":"f40c894f-96e0-4668-aefe-0d2dc5fb39e9","resolution":{"observed_at":"2026-08-06T15:37:02.007007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.980002Z","title":"Adaptive batch size for federated learning in resource-constrained edge computing,","venue":null,"work_id":"e850c1d4-0b86-4b95-b5c7-18ccc6ac29ce","year":2021},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.095112Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:53fd25578a8bda7ba57086c6364fdf05648acaab65b9adca589604a60e858625","observation_id":"f12bd5fe-d6ed-4d45-b9dc-b5e6c3a8f96a","resolution":{"observed_at":"2026-08-06T15:37:01.987654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.958539Z","title":"AMBLE: Adjusting mini-batch and local epoch for federated learning with heterogeneous devices,","venue":null,"work_id":"16c1bbd9-2fbb-435e-b883-3327ffebe3fd","year":2022},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.104145Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:83d2439647ffb2659e5db0a6941f0953e5f6bdeb4e7f5f4f80ecc8443ad1f08d","observation_id":"31f09d37-217f-4d6e-b29a-6bae404c6465","resolution":{"observed_at":"2026-08-06T15:37:01.963176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.937849Z","title":"Optimal batch allocation for wireless federated learning,","venue":null,"work_id":"0ff74b28-94f0-454a-b2d2-290b2f4e5b53","year":2024},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.110360Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:099d3f3398e09d522eff03955d56a6bacc04cbbf73f744711441935eb700719c","observation_id":"52852d83-611c-4cfa-8634-415a8d04a318","resolution":{"observed_at":"2026-08-06T15:37:01.944062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.919577Z","title":"Accelerating DNN training in wireless federated edge learning systems,","venue":null,"work_id":"815304d1-d921-4fbf-beff-bddfa1788c8f","year":2020},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.116111Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:63bd2ccddae4517ca3c245cd9d4b80a18505a20fe5fdca0f38c2e9bed0e83176","observation_id":"bcd4bf94-6cff-4eba-8045-ba7c8d010051","resolution":{"observed_at":"2026-08-06T15:37:01.924433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.902170Z","title":"Adaptive batchsize selection and gradient compression for wireless federated learning,","venue":null,"work_id":"e0c4c053-629c-461b-b002-270f65b758d6","year":2020},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.121906Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:fd86a9d33bb4642f24d97a465f8714487749b5858b06bb8fa37f95d7efed99c4","observation_id":"17772427-4c5b-47f2-9431-a6437275b40a","resolution":{"observed_at":"2026-08-06T15:37:01.907488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.879874Z","title":"DYNAMITE: Dynamic interplay of mini-batch size and aggregation frequency for federated learning with static and streaming datasets,","venue":null,"work_id":"55a9298e-0ceb-4c12-a45b-1925c2b178c6","year":2023},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.127978Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:805ffdcf0208e54134bb4b49554819c26d2369439569d644e28ddbc30d8056b2","observation_id":"96e0fce2-d3a8-498f-baac-19dee4716782","resolution":{"observed_at":"2026-08-06T15:37:01.888296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.01012","last_updated":"2018-05-16T22:38:30Z","snapshot_observed_at":"2026-08-14T20:43:30.904699Z","submitted_at":"2017-08-03T06:18:36Z","title":"On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization","version":3},"cited_work":{"arxiv_id":"1708.01012","doi":null,"metadata_source":"pith","pith_arxiv_id":"1708.01012","snapshot_observed_at":"2026-08-06T15:37:01.443026Z","title":"On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization","venue":"cs.LG","work_id":"3acd1e93-1e1f-46d2-b6a8-877fc53f136f","year":2017},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.133527Z"},"links":{"cited_paper":"/paper/1708.01012","citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:98210fad2b796216ee60b7802f59ae8b5a60410cfdae0cca213b267d5ece9d3e","observation_id":"03248b12-57f2-4bb7-8be2-3a3a0c57b694","resolution":{"observed_at":"2026-08-06T15:37:01.451148Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.862571Z","title":"Parallel restarted SGD with faster con- vergence and less communication: Demystifying why model averaging works for deep learning,","venue":null,"work_id":"c5d8af2e-ad15-4ad9-9e2f-4bd7fb60f5fa","year":2019},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.138639Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:12555c9ea05e2882d8753235ee29f3f5d903d6519eaf182db630a74181ec8d79","observation_id":"bcc73cc1-c011-4380-abfb-5ed1d73bd75a","resolution":{"observed_at":"2026-08-06T15:37:01.867539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.842755Z","title":"One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis,","venue":null,"work_id":"5cca6e30-3e26-48e0-b91d-17f357278e36","year":2021},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.143287Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:cc2e28af1511cd9635d88985d227ba5850eb8d0fe1e6dcf5586bc806a70a64bc","observation_id":"8ceca58d-6546-4941-8c1d-4bf53fa80b42","resolution":{"observed_at":"2026-08-06T15:37:01.848904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.822663Z","title":"A method for the solution of certain non-linear problems in least squares,","venue":null,"work_id":"80bf3472-1c05-4052-a87a-1d86f4df6b39","year":1944},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.149458Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:0281898bd83a28ff18adcfbdef1758ade0bbdfab5fff130c12573106b023c8b6","observation_id":"3ba11c7a-060c-4bfc-98b7-a12bede51f12","resolution":{"observed_at":"2026-08-06T15:37:01.828437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.790269Z","title":null,"venue":null,"work_id":"ce6c9d5c-bc29-4665-bdaf-fb896f293b59","year":2002},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.154774Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:d22514eba1141044d010320d40c86dcd16abdbab57c3553b92c9b5da5c06c30a","observation_id":"35267146-32a4-40d0-9aa0-211ff634e27a","resolution":{"observed_at":"2026-08-06T15:37:01.796085Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.13360","last_updated":"2025-07-28T11:28:08Z","snapshot_observed_at":"2026-08-16T13:33:07.735143Z","submitted_at":"2024-07-18T10:01:22Z","title":"Ultra-Low-Latency Edge Inference for Distributed Sensing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.13360","snapshot_observed_at":"2026-08-06T15:37:01.159038Z","title":"Ultra- low-latency edge inference for distributed sensing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.159038Z"},"links":{"cited_paper":"/paper/2407.13360","citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:37c9a027ec5e21a5ba3ce3ae8fe6dbe6331cb597ccc52daa567f8b18aeb36db9","observation_id":"a08dc60b-0623-4f82-9f19-a6eebe39971e","resolution":{"observed_at":"2026-08-06T15:37:01.159038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.770297Z","title":"Accessed: Oct","venue":null,"work_id":"a5f604be-5da1-420b-8d6d-f298db77d472","year":2024},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.164377Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:c587aab7f2d36414150d647494e14428b2cc6d3e5e039c29418e85de149f0196","observation_id":"369b842a-7cd4-4bde-aa8b-15eaa9d332ba","resolution":{"observed_at":"2026-08-06T15:37:01.775272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.752174Z","title":"Accessed: Oct","venue":null,"work_id":"2e28c115-9ae6-4b01-9d77-d4561b7b2668","year":2024},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.169269Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:ef6de84faa032a8b886aff4c9eddc8c3ca41307df14750dbe333c31685ae6e4e","observation_id":"27152bde-f955-4963-8141-f277170a1c35","resolution":{"observed_at":"2026-08-06T15:37:01.757417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.735426Z","title":"Gradient-based learning applied to document recognition,","venue":null,"work_id":"d7186a57-95c4-4059-a701-1226274371cc","year":1998},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.173898Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:602d0cc2efb615365ac9967efbe47a90e6a39f4b0a4696765310f0e78103cf09","observation_id":"6886ecac-b194-4714-b23c-98da5447d332","resolution":{"observed_at":"2026-08-06T15:37:01.740111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.179690Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.179690Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:3962b1aee942162dba1af5337db47f181a75cf71fa195b76c9bbcba97dc93140","observation_id":"e7fc5793-0144-41a9-88b1-ccf635c80bd8","resolution":{"observed_at":"2026-08-06T15:37:01.179690Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.702777Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":"ffa24144-24f2-49e1-be3c-8aa1286c26b0","year":2016},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.184782Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:671d5c0b91df6f17b5a8f8eb526b79721ba2d3191bb8efb43f143352185953e4","observation_id":"37c2732d-6fc6-4b14-8db2-0fccb598c518","resolution":{"observed_at":"2026-08-06T15:37:01.711126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:37:01.189240Z","title":"Revisiting outage for edge inference systems,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T15:37:01.189240Z"},"links":{"citing_paper":"/paper/2507.15601"},"observation_digest":"sha256:18634a0d68fe63badc318b905dab85269fc1373303192068df30a40f491177e4","observation_id":"3fca37bc-2096-4fc7-9a95-20d6a1d84b6f","resolution":{"observed_at":"2026-08-06T15:37:01.189240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.15601","last_updated":"2025-08-22T10:51:55Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T07:59:10.502866Z","submitted_at":"2025-07-21T13:24:38Z","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":2,"verified_fuzzy":35},"total_outbound_references":49},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2507.15601."}