{"as_of":"2026-08-15T08:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0213d998ef130da21e1ee7e9b1925a1b8c060e4f9b04cf9d0dcea57e0fdb4a73","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T06:08:36.870350Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.06090/citation-record","integrity":"/paper/2506.06090/integrity","json":"/paper/2506.06090/citation-record.json","paper":"/paper/2506.06090"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T06:08:41.447713Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":"f194c0ff-c431-48ea-ac58-7d3a251254d5","year":2017},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:34.452164Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:baa16df50e6985e594496e72280b6ea587fa7c43cde14784bac560c29941d918","observation_id":"5d0b3abd-8fc0-468e-9442-90b6f1e9f2e3","resolution":{"observed_at":"2026-08-07T06:08:41.513233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00582","last_updated":"2022-07-21T12:33:15Z","snapshot_observed_at":"2026-08-15T04:44:38.370440Z","submitted_at":"2018-06-02T04:45:58Z","title":"Federated Learning with Non-IID Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.00582","snapshot_observed_at":"2026-08-07T06:08:34.489006Z","title":"Federated learning with non-iid data,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:34.489006Z"},"links":{"cited_paper":"/paper/1806.00582","citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:8645b6a780b2a035c047676a2f8ad013213f8c6ff039603c2cb90a02812928f1","observation_id":"69ac4eb7-54ec-4421-a07c-0c3925e49e6a","resolution":{"observed_at":"2026-08-07T06:08:34.489006Z","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":"2026-08-07T06:08:41.286517Z","title":"Federated optimization in heterogeneous networks,","venue":null,"work_id":"a056f3d2-ab94-4928-9f94-39d088456cc6","year":2020},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:34.568266Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:811370266f6007d1e57914d6f35a0d7d266d8a9377e0485c1d4bee8054c3fae7","observation_id":"143fc9a3-a4c3-49e3-92f2-55304cc387e0","resolution":{"observed_at":"2026-08-07T06:08:41.326418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:41.087646Z","title":"SCAFFOLD: Stochastic controlled averaging for federated learning,","venue":null,"work_id":"279b6149-f4a6-4195-938b-0b58c5e97f86","year":2020},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:34.644132Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:2b84c527e943cb59e776125640b9b30dee0244437ba1ee382ca8280dcd2bb337","observation_id":"e9592a5a-2683-4c69-a14f-bd06df5c70da","resolution":{"observed_at":"2026-08-07T06:08:41.188656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:40.957119Z","title":"Tackling the ob- jective inconsistency problem in heterogeneous federated optimization,","venue":null,"work_id":"74a610d9-4bee-41a1-b57d-ef481fc71216","year":2020},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:34.770296Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:739dcbc3a0ee65af852b1e80a367287ca819faa7613a0d3d2bc49a1d95e7f80d","observation_id":"b848694d-765c-4d48-a58a-9cb8fdc468a2","resolution":{"observed_at":"2026-08-07T06:08:40.998046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:40.620140Z","title":"Model-contrastive federated learning,","venue":null,"work_id":"0419d394-e113-48e2-8860-dfb021840577","year":2021},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:34.858567Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:4bca6df7309503bec709628fb017f8feec975266980f6bd1f5ec4d86e92f2c0a","observation_id":"43cff166-2e4b-474c-bfcc-ef84ef9cb718","resolution":{"observed_at":"2026-08-07T06:08:40.847936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:40.493245Z","title":"Federated learning based on dynamic regularization,","venue":null,"work_id":"5d93768e-2d84-4944-bd81-7c7ffb3e4763","year":2021},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:34.923806Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:8e7d696e9e78a842414867671ce8308441e6ea695d604bcb6628a3be5d105cef","observation_id":"7a2595a0-7289-4497-bb5c-dad7ecfb3e55","resolution":{"observed_at":"2026-08-07T06:08:40.550415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:40.370316Z","title":"Federated learning from small datasets,","venue":null,"work_id":"af68cc36-9915-470b-8fd9-147e314f2b62","year":2023},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.003677Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:916ad6b567f58d6c5b6d8d57cbc5a1fd477a64dd06fb04eb62af3eac477c9def","observation_id":"761f3f1b-affa-4b49-a932-0f5638a6becf","resolution":{"observed_at":"2026-08-07T06:08:40.417045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.13267","last_updated":"2023-04-26T03:41:17Z","snapshot_observed_at":"2026-08-13T11:55:01.044735Z","submitted_at":"2023-04-26T03:41:17Z","title":"Bayesian Federated Learning: A Survey","version":1},"cited_work":{"arxiv_id":"2304.13267","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.13267","snapshot_observed_at":"2026-08-07T06:08:37.175048Z","title":"Bayesian Federated Learning: A Survey","venue":"cs.LG","work_id":"04600b42-bba4-4eaf-b226-4182c9b7d8da","year":2023},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.069888Z"},"links":{"cited_paper":"/paper/2304.13267","citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:723bfbb6c1828ca971efee140354395d02a866c079d125d82606c90a8ad33f7b","observation_id":"9cf9518e-f8b2-4502-b65f-d6e12ea7ba36","resolution":{"observed_at":"2026-08-07T06:08:37.234099Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:40.125958Z","title":"A Bayesian federated learning framework with online laplace approximation,","venue":null,"work_id":"e2dab113-99cb-4982-b295-96d87043e945","year":2024},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.191212Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:7cae116be3a01dd532b89f890d657d87086a29a74bcc5166a8a49b267898f01f","observation_id":"c86f5439-76c2-4ee0-876b-df095bf5330b","resolution":{"observed_at":"2026-08-07T06:08:40.206113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:39.867621Z","title":"Federated learning over wireless fading channels,","venue":null,"work_id":"11104e2d-0955-421f-a1bc-6726dde6fc71","year":2020},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.285554Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:bccf8e0f125cf9538cba32f7963ef66f91c119145ca412ac053432ca8348ff74","observation_id":"3785136b-4f2f-4d57-a022-2540f2092e62","resolution":{"observed_at":"2026-08-07T06:08:40.016574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:39.665774Z","title":"Broadband analog aggregation for low-latency federated edge learning,","venue":null,"work_id":"aac8a71b-9e4c-447a-a57c-affc9ff552ff","year":2020},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.347681Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:dac12164c5c04018c9ac0462b72fd6bff7c90eeceff980c8a63e4b8420f24e25","observation_id":"e392826e-2335-4fb4-aecf-1bf5ee5627c8","resolution":{"observed_at":"2026-08-07T06:08:39.766553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:39.368746Z","title":"Base station dataset-assisted broad- band over-the-air aggregation for communication-efficient federated learning,","venue":null,"work_id":"ac4059b7-0712-42fc-b107-0df1175d2b9d","year":2023},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.428022Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:03c2dae9f9d092d7f96509dd7bee688e1eff9d1506d13eed9fa0cbe720c36ebf","observation_id":"06917569-4ca9-4823-8895-220effb5c7f1","resolution":{"observed_at":"2026-08-07T06:08:39.515957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:39.108445Z","title":"Over-the-air aggregation-based federated learning in cache- enabled wireless edge networks,","venue":null,"work_id":"333f1b74-b297-4036-8681-082ca11b0e1a","year":2023},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.480083Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:e04626d3a86ed8bcc83a7dde1a3325900d038854f0deb5aadc10a47ce8015036","observation_id":"41547c7c-c575-40bc-a723-600432b5dc4b","resolution":{"observed_at":"2026-08-07T06:08:39.201014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:38.825113Z","title":"Optimized power control design for over-the-air federated edge learning,","venue":null,"work_id":"54e4456b-1a0c-462e-9788-9bae4a5ae5d3","year":2022},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.572416Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:bd6cce1f5be53b28c124fa5387300846c2eb9c82c2cf15b5e964d615555e6845","observation_id":"e11af8ec-2bc4-4a34-b5e5-feedf66f9efd","resolution":{"observed_at":"2026-08-07T06:08:38.946733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:38.611755Z","title":"Optimized power control for over-the-air computation in fading channels,","venue":null,"work_id":"4dad3b5f-8990-4f3d-bfbe-3805da002ec6","year":2020},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.669773Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:f5c7611373e4fde486ecda428e3c0295ef7e760eaadc9b372797484739e6c65d","observation_id":"cadb5c9c-45da-4547-9aa4-6e068143126f","resolution":{"observed_at":"2026-08-07T06:08:38.697482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:38.481166Z","title":"One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis,","venue":null,"work_id":"472cf40d-f458-4852-a0b9-5c1958ff0eec","year":2021},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.762367Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:9d27b2aa33710f3b5398708aae8ddf754cf802cf7d270ac85c484b2698fcb8eb","observation_id":"d51c471d-0bf4-4abd-a017-c7ed707aecce","resolution":{"observed_at":"2026-08-07T06:08:38.542238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:35.854062Z","title":"Privacy-enhanced over-the-air federated learning via client-driven power balancing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.854062Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:cbb0da349221041f599eb73f3515df6afc3388ac9d68348d8b6f5c5a89c48812","observation_id":"b5bca7fa-bdd7-4aad-8258-95b5edb64b1b","resolution":{"observed_at":"2026-08-07T06:08:35.854062Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.17413","last_updated":"2023-03-30T14:35:13Z","snapshot_observed_at":"2026-08-13T12:13:13.623888Z","submitted_at":"2023-03-30T14:35:13Z","title":"Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation","version":1},"cited_work":{"arxiv_id":"2303.17413","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.17413","snapshot_observed_at":"2026-08-07T06:08:36.973557Z","title":"Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation","venue":"eess.SP","work_id":"ac2c44a5-f68b-4b89-bf9c-04a6787b05fc","year":2023},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:35.950200Z"},"links":{"cited_paper":"/paper/2303.17413","citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:c2019c0d72083a298bdae04f796089ff17cdc75947ca27d9109fab42e64bf2f9","observation_id":"a7be6cc4-cfd7-4e7b-b749-45ea2055b794","resolution":{"observed_at":"2026-08-07T06:08:37.011632Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:38.359254Z","title":"Bayesian aircomp with sign-alignment precoding for wireless federated learning,","venue":null,"work_id":"5daea8c6-4c07-4433-ae88-d01e998443dc","year":2021},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:36.048279Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:77f0e5f1196a269a750d2c41a1739abf0878a32a9ebe20563e0f9e70d3fe4d8b","observation_id":"02266568-bdaa-48eb-99c1-516c490f8e4f","resolution":{"observed_at":"2026-08-07T06:08:38.418971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:38.218295Z","title":"Bayesian over-the-air computa- tion,","venue":null,"work_id":"ba5d3a55-126e-4810-bb7f-25f62ac20216","year":2023},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:36.151670Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:46521989ce44ac179a4697858cd3a19c768d2d43286efc93f227e3266d723dff","observation_id":"69f0878d-54f2-471a-9eaf-eaecc5411666","resolution":{"observed_at":"2026-08-07T06:08:38.291207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:38.074360Z","title":"Practical variational inference for neural networks,","venue":null,"work_id":"80fd609b-e48f-4ac1-aff1-7654a9a9db2d","year":2011},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:36.246514Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:f3774938df1d2f0d8ea5a6fb5ddee733e9c6718a1da122d9c2ff7e912dcc5da3","observation_id":"dbf710c1-8909-4353-bc1b-8cde11f5de76","resolution":{"observed_at":"2026-08-07T06:08:38.148228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:37.940828Z","title":"Weight uncertainty in neural networks,","venue":null,"work_id":"d17671db-6d8d-474a-b4f2-ea5f574c5983","year":2015},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:36.333989Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:1da7b9bdc812a8b86678ca6b17925c2bd6949f0e1ba75fd2b582c10efeec44fc","observation_id":"90eb393b-207f-4192-828b-cfa56156954a","resolution":{"observed_at":"2026-08-07T06:08:38.018043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:37.752676Z","title":"Bayesian inverse contextual reasoning for heterogeneous semantics- native communication,","venue":null,"work_id":"88e793f3-38b2-4bab-92c5-450ef08afbd4","year":2024},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:36.413182Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:894220c53ba6f141f9bad2c05e9ec401bdec535c8acbfe995bbba6af0aaa0548","observation_id":"9192e1d3-4df8-4e13-90d3-28e887bc15f1","resolution":{"observed_at":"2026-08-07T06:08:37.829662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:37.581843Z","title":"Conflations of probability distributions,","venue":null,"work_id":"aec08279-d4f7-48ea-87e9-7ebe2f5d62ad","year":2011},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:36.509779Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:ad13b172766fbdcfad14d0626ba3bc80295c190c4d071173ccdb535340e5ad67","observation_id":"0f71f48f-0e65-4309-8f33-2edccf5705c1","resolution":{"observed_at":"2026-08-07T06:08:37.657185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:37.456115Z","title":"How to combine variational bayesian networks in federated learning,","venue":null,"work_id":"af413e96-99d1-4585-91d6-85d350b1d9f8","year":2022},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:36.622290Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:e5019ecab999d6cc5263eb7b963afe8bd58ad1dd280a516021b88cd459bf27a2","observation_id":"dcd6e6da-1298-4b54-bffc-4f21508b9fd2","resolution":{"observed_at":"2026-08-07T06:08:37.509551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.04345","last_updated":"2026-06-01T13:09:23Z","snapshot_observed_at":"2026-08-13T12:29:24.864742Z","submitted_at":"2023-03-08T02:52:40Z","title":"Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.04345","snapshot_observed_at":"2026-08-07T06:08:36.687156Z","title":"Federated learning via variational bayesian inference: Personalization, sparsity and clustering,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:36.687156Z"},"links":{"cited_paper":"/paper/2303.04345","citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:6a3ee3d9c983215d651c3a4ee460de3dfd71cff6f197ca493d43c1c1f5a9de4d","observation_id":"f71112d2-46a8-4b16-8680-d297538803f5","resolution":{"observed_at":"2026-08-07T06:08:36.687156Z","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":"2026-08-07T06:08:37.333948Z","title":"Blitz - bayesian layers in torch zoo (a bayesian deep learing library for torch),","venue":null,"work_id":"4f622314-8993-47ee-a4e3-8ce990974547","year":2020},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:36.789476Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:2b47d20f7bdb2a9a280a96378d9f7cb2b6332a575a27746a3cfa387a988d4cf3","observation_id":"b699801f-767c-43d0-a578-8d7a9bd114ea","resolution":{"observed_at":"2026-08-07T06:08:37.379059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T06:08:36.870350Z","title":"On calibration of modern neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:36.870350Z"},"links":{"citing_paper":"/paper/2506.06090"},"observation_digest":"sha256:c666e090e1f39c1009d34ab720983cd7cd57abe75a1e0499c3523d2e488664f4","observation_id":"36be8a16-0b14-44b4-acdf-238aa5eee8c2","resolution":{"observed_at":"2026-08-07T06:08:36.870350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.06090","last_updated":"2025-06-06T13:50:22Z","latest_version":1,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-14T11:09:19.744770Z","submitted_at":"2025-06-06T13:50:22Z","title":"Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":2,"verified_fuzzy":23},"total_outbound_references":29},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.06090."}