{"as_of":"2026-08-10T09:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c3dd3ade49380b35307cfdde9c6fabac3760bd08cb92dc875b9c27e53af8e95b","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:29:14.718066Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2507.05704/citation-record","integrity":"/paper/2507.05704/integrity","json":"/paper/2507.05704/citation-record.json","paper":"/paper/2507.05704"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1809.00343","last_updated":"2018-09-02T14:18:40Z","snapshot_observed_at":"2026-08-06T21:37:36.808736Z","submitted_at":"2018-09-02T14:18:40Z","title":"Towards an Intelligent Edge: Wireless Communication Meets Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.00343","snapshot_observed_at":"2026-08-06T19:29:10.312629Z","title":"Towar ds an intelligent edge: Wireless communication meets machine learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:10.312629Z"},"links":{"cited_paper":"/paper/1809.00343","citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:40904324f1643b96b3bfa8c1b76517a6387c2aacc64a5c7eb155fc9506acabbb","observation_id":"f90af97b-9d83-4b47-9e23-1d0fcb656ca7","resolution":{"observed_at":"2026-08-06T19:29:10.312629Z","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":"2026-08-06T19:29:23.254667Z","title":"In- edge ai: Intelligentizing mobile edge computing, caching and co mmunication by federated learning,","venue":null,"work_id":"4a0f7fc3-4832-4026-b657-098d6386a5b4","year":2019},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:10.393089Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:e6e149c3105961a9b0d017c7f422b3cd07be98209e7b63cee5f1597f99a12e7a","observation_id":"9e1ee256-d635-4bf2-bcd5-54de8e9e806f","resolution":{"observed_at":"2026-08-06T19:29:23.443577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.02527","last_updated":"2016-10-08T13:25:15Z","snapshot_observed_at":"2026-07-06T05:13:51.685562Z","submitted_at":"2016-10-08T13:25:15Z","title":"Federated Optimization: Distributed Machine Learning for On-Device Intelligence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.02527","snapshot_observed_at":"2026-08-06T19:29:10.474817Z","title":"Federated optimization: Distributed machine learning for on-device intelligence,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:10.474817Z"},"links":{"cited_paper":"/paper/1610.02527","citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:4919930d0f3a725133b14f10ab9545def085aa82cd80c0704e839568a3c48fd1","observation_id":"911c3e4e-cf31-4b0d-9f84-ae5aaff4ddab","resolution":{"observed_at":"2026-08-06T19:29:10.474817Z","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":"2026-08-06T19:29:23.004464Z","title":"Scaling distributed ma chine learning with the parameter server,","venue":null,"work_id":"e2ca82a4-9727-476d-b04e-50e21a10297a","year":2014},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:10.579507Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:3350d8540c9d91c838c5b78581484bcf82b9d7d8e6bfce38bb701a938beedcbd","observation_id":"281156f4-9835-426f-981d-c2c95dbde58f","resolution":{"observed_at":"2026-08-06T19:29:23.141632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:22.681439Z","title":"Federated learning over wireless networks: Optimization model design and analysis,","venue":null,"work_id":"a4b1bed2-23da-4674-b354-e948abd2dde3","year":2019},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:10.706905Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:72eb0445744610e6f7ca6911e4b095f5dc44a3a303e16f55aa77f2d21bb56fc1","observation_id":"b3ce485a-6b45-426e-b838-ecc2dad15d68","resolution":{"observed_at":"2026-08-06T19:29:22.841143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:22.502106Z","title":"Energy-efficient federated edge learnin g with joint communication and computation design,","venue":null,"work_id":"c2a5bfc0-f4d4-4eff-a7ca-fb734256b1ca","year":2021},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:10.771354Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:ebbf38bb34deddd5364b6e883ae91e609f7a1822ffa722cbb7114ab113b9893f","observation_id":"ee12f3db-4ba3-4c0c-95a2-23991061466d","resolution":{"observed_at":"2026-08-06T19:29:22.581240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:22.259384Z","title":"Cost-e ffective federated learning in mobile edge networks,","venue":null,"work_id":"372dcb61-bc35-42a5-bb9e-6ec3b6c5414a","year":2021},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:10.869475Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:7bada49ad9518a2d1b4ac5aac4b85f663239dccc4bf8e68d2ed2dc77280805eb","observation_id":"edd8111e-1169-4237-9d2f-2bdc5fa77728","resolution":{"observed_at":"2026-08-06T19:29:22.378904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:21.972003Z","title":"Convergence time optimiza- tion for federated learning over wireless networks,","venue":null,"work_id":"acadf552-d782-46be-b3bc-1d5199597cab","year":2021},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:10.990809Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:8b6aa76c98d9254bd1c7a86f82a0ac93339e3831f8a267da65e8aca011d20ab8","observation_id":"6836ca06-932b-46b6-b4d4-2493cca59a70","resolution":{"observed_at":"2026-08-06T19:29:22.137804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:21.682773Z","title":"Energy efficient federated learning over wireless communication n etworks,","venue":null,"work_id":"85ede415-c4c0-4adc-bd54-3abe8066bb19","year":1935},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:11.128077Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:bc01c1acc26714bd91e1abf8d3f7147df841db81fb32493149d126aa9ad2c7bb","observation_id":"794deaad-6462-4a81-975d-a76c556ac77f","resolution":{"observed_at":"2026-08-06T19:29:21.843732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:21.425603Z","title":"Broadband analog aggrega tion for low-latency federated edge learning,","venue":null,"work_id":"e53e403a-df1f-4285-a752-5a8c53d0a4b7","year":2020},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:11.254365Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:428d3aac1e02faaa2c7a0517bf31658f93b27d26bf4b66e9ba84f674ec4bfa3f","observation_id":"5399a2ba-1f08-46e7-8c44-22d75d3310a4","resolution":{"observed_at":"2026-08-06T19:29:21.553541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:21.065637Z","title":"Communication-efficient learning of deep networks from de centralized data,","venue":null,"work_id":"f7ab81c7-5002-4aa3-844e-ec1faf75b27a","year":2017},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:11.372943Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:d04eb7d76e04618511f4f78e147350625165bc9a4b8913d31c8eddceaee18ab9","observation_id":"fb097bfb-cf14-4610-8954-a660c9fb0e55","resolution":{"observed_at":"2026-08-06T19:29:21.252960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:20.749790Z","title":"Joint optimization of com- munications and federated learning over the air,","venue":null,"work_id":"53d11567-bf4b-43ad-9268-0dd56b52ccb7","year":2022},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:11.521997Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:46cd4260af69aad0d9f645de6f61f3c82831b39c4d0d97f342ff5e0985d54456","observation_id":"a794a58f-eee5-4a8e-bb48-22cbe0ec213f","resolution":{"observed_at":"2026-08-06T19:29:20.904464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:20.484007Z","title":"Computation over multiple-ac cess channels,","venue":null,"work_id":"897b5d98-00de-4d07-a3ce-599ee70e7b8c","year":2007},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:11.626036Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:d65ebfedad98a10b79b441b04239fd8f6fa006f19c91e7ca1ce8fe4f95faa1d7","observation_id":"a17390d9-b91f-47e9-96cd-e790a458f38d","resolution":{"observed_at":"2026-08-06T19:29:20.605441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:20.150649Z","title":"Optimized powe r control design for over-the-air federated edge learning,","venue":null,"work_id":"90b04fa8-c465-490b-b3fe-ce012fb8f682","year":2021},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:11.744029Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:56950d1eb600de1705bb9916fffb81383ecee8787e6e102b5606b98c789f6030","observation_id":"2b6cbdc8-58b5-4afe-b101-24cee591d91f","resolution":{"observed_at":"2026-08-06T19:29:20.293772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:19.889006Z","title":"One-bit over-the -air aggre- gation for communication-efficient federated edge learnin g: Design and convergence analysis,","venue":null,"work_id":"6aa4e5f8-47b9-4044-89b1-e2e879f92025","year":2020},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:11.892604Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:881ebb23a87348dd8eb07acb76a5d8f30e0e6d15c33c7f9cd05b2387ad0599fd","observation_id":"8ade69fa-ab54-4d36-8436-970a9cba742f","resolution":{"observed_at":"2026-08-06T19:29:19.998072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:19.666575Z","title":"Federated learni ng via over- the-air computation,","venue":null,"work_id":"ea8be657-c770-421c-b45f-0553f7249cbf","year":2022},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:11.997419Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:b70ee92c640884374db795bcc7916afd26f336ba7e0a49d608f2244159bf647d","observation_id":"cc191500-39a6-462e-a1ba-92e9f72d5586","resolution":{"observed_at":"2026-08-06T19:29:19.791009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:19.451191Z","title":"Machine learning at the wirel ess edge: Distributed stochastic gradient descent over-the-air,","venue":null,"work_id":"086bd9b8-990a-4550-929f-4b2fc7e334f5","year":2020},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:12.123514Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:82867b69c0e19d03fe7e720cbde9bc06a1e08478dc1fec30e0beeea887f350d8","observation_id":"9c3d849f-dab9-4468-8f0e-8ee931d045ae","resolution":{"observed_at":"2026-08-06T19:29:19.543896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:19.225957Z","title":"Transmission power con trol for over- the-air federated averaging at network edge,","venue":null,"work_id":"8a234f1c-2072-42c1-831d-3ca2c824d307","year":2022},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:12.231779Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:f18c45ff9dfed52fd561b557f3ec58ef07a7363dfa4a6f78a42056c01588921d","observation_id":"05688b68-2f45-4743-b3f2-576b33fd9e10","resolution":{"observed_at":"2026-08-06T19:29:19.327334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:18.978264Z","title":"Federated learning over wire less fading channels,","venue":null,"work_id":"111af108-499c-449c-b316-3e0084ce1a46","year":2020},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:12.339383Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:d40aeed9b9d940069aea3894cc536a1ef61b089a769a65cef77f0d1250c53ed3","observation_id":"442aa938-708f-4581-abf3-6c373c5bcce1","resolution":{"observed_at":"2026-08-06T19:29:19.084166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:18.721660Z","title":"SA FA: a semi-asynchronous protocol for fast federated learning w ith low overhead,","venue":null,"work_id":"a513e037-2439-4d81-bcc3-adc4f4a93a8b","year":2020},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:12.441322Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:e3867116703581b450ab8fcdb7054e421a6377a10d3d991777dcdb22f492c28d","observation_id":"5459b385-a4ad-4a60-ae2b-51a94d946d79","resolution":{"observed_at":"2026-08-06T19:29:18.818334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.03934","last_updated":"2020-12-05T01:33:57Z","snapshot_observed_at":"2026-08-09T06:49:53.460129Z","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":"2026-08-06T19:29:12.546626Z","title":"Asynchronous federate d optimization,","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:12.546626Z"},"links":{"cited_paper":"/paper/1903.03934","citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:a0f0c5b78f10c6ce775274fa7caf0ff02ff0b6c9c7509d9b3ec6e5a8f99d223b","observation_id":"b2bd8a54-4944-45d8-9dcb-f2e9de17d9c6","resolution":{"observed_at":"2026-08-06T19:29:12.546626Z","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":"2026-08-06T19:29:18.465443Z","title":"Asynchro nous online federated learning for edge devices with non-iid data,","venue":null,"work_id":"bfa2327e-d085-49cd-86ac-c97677a95c42","year":2020},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:12.678336Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:9ea4c2b568c6625e8b359c4c503720c8ae12e51d7eef91ea7a0f329549223ed1","observation_id":"180dd24a-e98e-40e6-b8df-628e2586ba6c","resolution":{"observed_at":"2026-08-06T19:29:18.607368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:18.236015Z","title":"Asynchronous stochastic gradient descent with delay comp ensation,","venue":null,"work_id":"54ae47a2-1938-4c08-940f-03a05cd2119c","year":2017},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:12.798400Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:58a82ae387ee4116d5a05e38ad61c2a35d6620dd507a9033d498c52ce202e28a","observation_id":"50624753-358f-4e80-a144-e5380754cd52","resolution":{"observed_at":"2026-08-06T19:29:18.348628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:17.991170Z","title":"Client selection with staleness compensation in asynchronous federated learning,","venue":null,"work_id":"aa1f13d7-7ab9-491c-827e-e6b80d7bf527","year":2022},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:12.906716Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:424d63474a74c2f9dba649347b56e1f9c4780d998f1277f715c05a3a6469f0b2","observation_id":"390e8c2e-be86-4321-bef1-bd98ba74c28f","resolution":{"observed_at":"2026-08-06T19:29:18.116043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:17.757788Z","title":"FedS A: A semi-asynchronous federated learning mechanism in heter ogeneous edge computing,","venue":null,"work_id":"cb2f5a26-cb10-4ee8-b6fb-beab75e9b9e7","year":2021},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:13.031306Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:b8744347890ce4bcb092c4ae38895eb6bbbefe4fee673b7caae9b527a525ed4a","observation_id":"6d303f74-23da-4e74-9980-20843faa7d71","resolution":{"observed_at":"2026-08-06T19:29:17.872629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:17.620077Z","title":"Tifl: A tier-based federated learning system,","venue":null,"work_id":"a2b2b28f-ba58-4d49-b54c-85058f7a3ac3","year":2020},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:13.109253Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:a3492bbcc337e145d85a1f1f99781f84bc1a4f467abd4398fff049c036c77c5f","observation_id":"554c0608-0c26-4109-b8a2-aeb22a2d64ea","resolution":{"observed_at":"2026-08-06T19:29:17.690454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:17.385965Z","title":"Adaptive asynchronous federated learning in resource-co nstrained edge computing,","venue":null,"work_id":"65dafec6-b938-45cf-82fa-5a656b8d08a9","year":2021},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:13.209037Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:23237fb58f58ff52caf3a7518610703e70bd5a797ccad4fd559cda3f926a5e8c","observation_id":"1d384c50-8e42-44b7-aae6-a20f5385cbe5","resolution":{"observed_at":"2026-08-06T19:29:17.509372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:17.141772Z","title":"Decen tral- ized federated learning with adaptive configuration for het erogeneous participants,","venue":null,"work_id":"c42c5c7d-2de0-4acd-95c7-a569e9d515b3","year":2023},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:13.319442Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:ac9c8aecf7a448bf763dcd5003f62290d6dea35b7fec390b99c9a84464cddbc6","observation_id":"0e3cef46-0a56-4bce-953e-a05b97de67c4","resolution":{"observed_at":"2026-08-06T19:29:17.238278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:16.911999Z","title":"Gradient statistics aware power co ntrol for over- the-air federated learning,","venue":null,"work_id":"8a91f63c-037d-400b-a33d-fd71f12ef00c","year":2021},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:13.418784Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:7bc91bdfce1027df566366713f4da446ea8406bf1ac4421bc9b73225e035a882","observation_id":"864cd6bc-c446-4861-a2f7-15a576aadfe0","resolution":{"observed_at":"2026-08-06T19:29:17.027282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:16.637741Z","title":"On the pairing of the soft max activation and cross-entropy penalty functions and the der ivation of the softmax activation function,","venue":null,"work_id":"5ee69e03-5f68-465d-9b0c-e811ce21dfe3","year":1997},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:13.544587Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:b68156ae93dcc49d0fbbbf8e9bd032dfc72a6a237d856343e4645f037a38c7fb","observation_id":"9bf73a5d-64f6-4483-acd0-1f1df79dcd83","resolution":{"observed_at":"2026-08-06T19:29:16.769187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:16.349080Z","title":"Dynamic scheduli ng for over- the-air federated edge learning with energy constraints,","venue":null,"work_id":"c59a650a-258d-4a2b-a636-e8d27a8fde1f","year":2022},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:13.664148Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:ebe6443502e53bd98156b53fb1d1bfacaac603c8cfd23232ca08a34903b215fd","observation_id":"abd77e04-1201-4c96-b898-352c28ba4f44","resolution":{"observed_at":"2026-08-06T19:29:16.502269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00582","last_updated":"2022-07-21T12:33:15Z","snapshot_observed_at":"2026-07-06T06:42:35.645776Z","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":"2026-08-06T19:29:13.805515Z","title":"Federated learning with non-iid data,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:13.805515Z"},"links":{"cited_paper":"/paper/1806.00582","citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:90567a4da2823854d0fbaa9b227987141f45eef4383a9a9f23fdfba0e98f7933","observation_id":"f210a90f-cf15-4624-be10-89fe076b13fc","resolution":{"observed_at":"2026-08-06T19:29:13.805515Z","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":"2026-08-06T19:29:16.037816Z","title":"A de layed proximal gradient method with linear convergence rate,","venue":null,"work_id":"78f9c8e6-6203-45a9-92be-ad7d48c5d0b0","year":2014},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:13.925551Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:1309a652f5d9d31054e63d0ffc9e365f54ba3c2b53c8e6e07efbd68169f1e713","observation_id":"6ee91a40-8c7e-44e1-add4-bfbaecb7394b","resolution":{"observed_at":"2026-08-06T19:29:16.190851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:15.828054Z","title":"Gradient-based learning applied to document recognition,","venue":null,"work_id":"e6999391-1352-4fa1-a35f-eeb4efc925cb","year":1998},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:14.027655Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:970b3ac05dd54076c5d59ae717437ac3270af0cee2bf2981aefc96f2dc6cf431","observation_id":"4f462c9d-0c48-49f6-a8fb-a30261d59dc5","resolution":{"observed_at":"2026-08-06T19:29:15.902456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:15.620409Z","title":"Krizhevsky, G","venue":null,"work_id":"391a491a-df00-416e-b17f-b6458b4c21c2","year":2009},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:14.140233Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:082cb211da9fb08497fc989856e07cd931ed134233aa4172ce37fcc56cd348f8","observation_id":"1a68fb65-de47-47ef-a7b7-a0cf00ef701f","resolution":{"observed_at":"2026-08-06T19:29:15.715682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:15.391626Z","title":"Imagenet large scale visual recognition challenge,","venue":null,"work_id":"28931a20-3a46-487d-b1aa-da6af3239d33","year":2015},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:14.257830Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:e78aa2b3caf1a26b5ac2cdec2fcd72e0659b1b6416c220d54949dd5aa3156588","observation_id":"5e84b359-ff88-4a6a-81c0-5f8d9e11d855","resolution":{"observed_at":"2026-08-06T19:29:15.495518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:15.159553Z","title":"FedUC: A Unified C luster- ing Approach for Hierarchical Federated Learning,","venue":null,"work_id":"28b73e14-efcf-4a49-8708-7688606e0d95","year":2024},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:14.383903Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:2cbb4ed3d9d58f61160f7e2e560c7f1007351ac46e41e3d91b37d2b5a0d8d619","observation_id":"2203d6cc-89ff-4f20-bd23-d7095ff682c0","resolution":{"observed_at":"2026-08-06T19:29:15.275241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:14.893228Z","title":null,"venue":null,"work_id":"54d429bf-39b3-4cef-8d9d-7fdee1cf582f","year":2013},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:14.497575Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:6064f3e1d28e03a1e473a20e225c9ae3f319647966b639b06a03ea39c98b7038","observation_id":"062180c2-fd13-4a50-9313-d7813994ff96","resolution":{"observed_at":"2026-08-06T19:29:15.012253Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T19:29:14.612253Z","title":"Shalev-Shwartz and S","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:14.612253Z"},"links":{"citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:963a4ae8c35c62195e96c48af51799ac5fa0f949d3e0964f2cdd52c3ba99a216","observation_id":"b1a5a6e0-c223-415b-a7f2-61cb5f9de2de","resolution":{"observed_at":"2026-08-06T19:29:14.612253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-07-06T03:53:32.549552Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-06T19:29:14.718066Z","title":"V ery deep convolutional networks for large-scale image recognition,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T19:29:14.718066Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2507.05704"},"observation_digest":"sha256:1825e4f1816ab37adb8bf8e4f0533f839518d52241551bd94b785c7493b1ab71","observation_id":"931cdb7a-6a38-4253-9c10-c89da4527dc6","resolution":{"observed_at":"2026-08-06T19:29:14.718066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.05704","last_updated":"2025-07-08T06:27:39Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-10T05:06:20.421672Z","submitted_at":"2025-07-08T06:27:39Z","title":"Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":33},"total_outbound_references":40},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2507.05704."}