{"as_of":"2026-08-07T10:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b70a270e9db8aa69e08015df854ecd7195dff142baa90547fb72ba835d745fe9","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":"2026-08-07T06:34:17.273281+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":"2026-08-06T19:17:39.864880Z","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:cb0278534a128398e10dde64438ec87aaf44d050caa87f065373ccbd96bcc729","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":"2026-08-06T19:17:39.864880Z","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":"sha256:0105ecc0072833379d1f349f42a309400f1e57128ff806f7a20e89895eba2fd4","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:76033bab83524b679fd8978cc17c17580839d1f5f99a832e5577bc2784bae077","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":"2026-08-06T19:17:39.864880Z","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":"sha256:62128e58d29c3451aee830512ce34670967d54244c3021e2a5c187fc98107989","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:47b0ffe5dd62d02965d49ac71cedfa4fa6f78458fc0bcdd995c989164e82996c","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:46fecdb0a7fac6f0ce4686cf5528e3650ca047357acd4c594f6b4991dcf9a2d2","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:5df61c3c9a8081a788258a650d29ffe9c43f70a1e27c7a0c0d92f86083b75da6","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:498bd0d19451c1e37aba44e1d14cd50412ba3e3fa4751db0b43e5604d4f4911e","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:5cd075f0c2f66f14393957e3b6ff56e3b512d926b2e995392e80bdc959891b92","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:053d173f2307b540b3fae641a2c089b92d8486aed7031d697bab74dae3caf6ab","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:b23b7c20fc5e7925be7762bba1897b3bacb5cfcadf8ef292381f105b32f7a1ba","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:9e971eb968a63208aad7a8297d32b1e63407387b98f9efd953ec032ca6e1671a","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:2ef4a27da44a068624714645fb97928fb493c51cf1eb94b4bcb4aa2bd351cc1e","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:6146817f27f3939b528f7aa79ffb1756e3a691a27376d83921a2999e0401a7c6","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:d2c2559f4ef3b76fdaaf9106ae54ea6adaf8509567e980c0c59eb77556ca911a","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:f1947345e7b90f86c01dafd433f506d5a60082b579d5dc27d06d1287fd997caa","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:d86131d0968c3317e7912bfc0fce0efe7861cf5d02a8feb9f055ff066b07b290","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:72999978d47c5e1d83594be7e507e601f0d853362b4508d47e78900fdb58c2ee","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:c990e245923f1d198da16fe3083c712d32f30e9b32f37816f462751aca8ea458","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:de3ac83c39dab41e2fa78508607254f1a7625b38cf46f94de2e3cbe272e1d120","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.03934","last_updated":"2020-12-05T01:33:57Z","snapshot_observed_at":"2026-08-06T09:53:05.342074Z","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":"2026-08-06T19:17:39.864880Z","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:dcf06e7b1cb4d939f31a3010522f02f06ba69eacb094718314e068ce4ec1071b","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":"2026-08-06T19:17:39.864880Z","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":"sha256:5f7007454d47c5c6c81f826bf1509235af631b3a73054c5df855aa0660a8cc83","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:f4993a3eef26609ed5b1136ba82b2b5aa55d418d1fee476a348287aeba9e01ab","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:833c719e744df1083a0e414e0175c792dd4979a32887aaee68fcb41971ecde4f","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:a7c15ca900611eb40c98358ba13271b307dd5a7ef331167e136db7392accaed2","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:47c3db141889b19a2216230aa5baa17913fb44feeccb992d45871defd8e0578a","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:d269d49cefe7f3b695a3281c32705f7c363a3cb93ddc052e5c3e71101745aeee","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:de16c11e9e028b9d81cf34c4527fa8ad5bf838c3538f72666eadaa76e9e82a3c","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:fdb14394b0a16a783d341f933fa34b044a0f161c52a78036a6583225cb3000b4","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:fe52a62405889e69b6bd6d20205552bfcda74d734695564f7db1d4833b617bb5","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:e91c11ac9e135270dbedddc59b27c8247e38316e749b1bec06b5bab8b7774927","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:79541255b507a2765ca3a3d51ab4f38c7990439736cf6a03a681809b744fb7b2","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":"2026-08-06T19:17:39.864880Z","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":"sha256:24704b1037da431e0ca0a6ae803226e1749baa1ab036e9992d640e07186171b1","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:46c4cf508dd6fb752a629b924e2f01987f1c471b892446086ca1c75a7a7c1e7b","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:b7fea599d88d6222ff0868fb4f36105beb77f12f2bbe3491717e19ba40ce90ab","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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:cdc0ef0bee865f0eaabbc352655fca9335ade71844c84af98254d8409ef2683a","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:788009455ab94b8e80bfb426af41c4f9c86c64d972de7a027e79edd4d49f6c6e","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:5dae493d7c61d9cd6f69c959e80a92f420eb0dc39568ba9c0db653ef1368c703","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 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T19:17:39.864880Z","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":"sha256:779a9dbe6eec3e1ec948eb253f918196741642f428df7362512c2d371868b1aa","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":"2026-08-06T19:17:39.864880Z","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:e73eba1e4500f5960093dab136482aebbf1b4924a87622237995b99c44c9a3fa","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":"2026-08-06T19:17:39.864880Z","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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2507.05704."}