{"as_of":"2026-08-13T07:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:413663db61b85b6bc71284eb8cd118a34e61df9336281d3d49b014c87af98f40","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T15:53:22.083057Z","state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2411.13861/citation-record","integrity":"/paper/2411.13861/integrity","json":"/paper/2411.13861/citation-record.json","paper":"/paper/2411.13861"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:53:22.822062Z","title":"Communication-Efficient Learning of Deep Networks from Decentral- ized Data,","venue":null,"work_id":"78a1f6ad-45f3-456a-bbe2-b7130fa3723e","year":2017},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.540717Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:e900d834d931809918ac989de226b6583eecd48fcb84f20fdbab15e27814933e","observation_id":"e88d3e3f-f06f-4890-b03e-96f4658a1b23","resolution":{"observed_at":"2026-08-12T15:53:22.825430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.05492","last_updated":"2017-10-30T20:52:14Z","snapshot_observed_at":"2026-07-06T05:15:00.158639Z","submitted_at":"2016-10-18T09:11:51Z","title":"Federated Learning: Strategies for Improving Communication Efficiency","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.05492","snapshot_observed_at":"2026-08-12T15:53:21.559254Z","title":"Federated learning: Strategies for improving communication efficiency,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.559254Z"},"links":{"cited_paper":"/paper/1610.05492","citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:78725a56fe6537590be24fc4c6abc0b552f8e26f36d2d3c5d43379b80e9dee1a","observation_id":"0e3ecf31-ae6d-4c28-ad36-73a25389d067","resolution":{"observed_at":"2026-08-12T15:53:21.559254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.09767","last_updated":"2019-05-03T12:58:04Z","snapshot_observed_at":"2026-07-06T06:41:03.009337Z","submitted_at":"2018-05-24T16:38:51Z","title":"Local SGD Converges Fast and Communicates Little","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.09767","snapshot_observed_at":"2026-08-12T15:53:21.585707Z","title":"Local sgd converges fast and communicates little,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.585707Z"},"links":{"cited_paper":"/paper/1805.09767","citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:6e77aca324e2215df2f2af74caf3a21724d27a2ae598e4cafa9a04a3d00ef6a5","observation_id":"53fd3733-51e9-4b0d-bc6d-c6f1d4c4b2c5","resolution":{"observed_at":"2026-08-12T15:53:21.585707Z","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":"2026-08-12T15:53:22.813702Z","title":"Parallel restarted SGD with faster con- vergence and less communication: Demystifying why model averaging works for deep learning,","venue":null,"work_id":"ab194be9-adc8-4fea-abac-425b60f56a0a","year":2019},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.588694Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:0ecb54cdd1355ff7c2827da5015818911aea55870c03c281663957aaf3764b54","observation_id":"3b8ccf51-901d-4e3e-b2ef-7b2da9e85266","resolution":{"observed_at":"2026-08-12T15:53:22.816605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.743581Z","title":"Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization,","venue":null,"work_id":"2848e3b9-aa21-4536-956b-1c32ac2348b6","year":2020},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.591909Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:c7670950d8ce06c9524f9d04c018ade70d9edc1164a8a88202673b88c7ae6c35","observation_id":"793cf2dd-41ba-4354-ba6b-70abc0d0a8c5","resolution":{"observed_at":"2026-08-12T15:53:22.801230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:21.595826Z","title":"To talk or to work: Dynamic batch sizes assisted time efficient federated learning over future mobile edge devices,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.595826Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:6ffd06d2f4590963bb8df2c7897c3c572eae1af46bf09498c75f4c11fc640b4d","observation_id":"50d01c60-eb53-4fcb-af33-02f4d89f32c7","resolution":{"observed_at":"2026-08-12T15:53:21.595826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02395","last_updated":"2024-04-03T01:42:30Z","snapshot_observed_at":"2026-08-13T00:38:51.170175Z","submitted_at":"2024-04-03T01:42:30Z","title":"Optimal Batch Allocation for Wireless Federated Learning","version":1},"cited_work":{"arxiv_id":"2404.02395","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.02395","snapshot_observed_at":"2026-08-12T15:53:22.105497Z","title":"Optimal Batch Allocation for Wireless Federated Learning","venue":"cs.LG","work_id":"381e322d-da93-44a3-a1dc-7b8a7f292889","year":2024},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.599505Z"},"links":{"cited_paper":"/paper/2404.02395","citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:8eaa1a5eb79e8533821c2f7ffcc0faaca353fd1b06d8baaf60844e263a337932","observation_id":"e5c7d21c-869d-46d1-8a37-dcf67279e3bf","resolution":{"observed_at":"2026-08-12T15:53:22.190873Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.687954Z","title":"A joint learning and communications framework for federated learning over wireless networks,","venue":null,"work_id":"9f84afa6-43c4-43d8-b3af-1c3f0cd7d889","year":2020},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.602866Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:a2a2449b6383ec426bf917b9b90a4a15fe2e868610917ee7338c2e841e86ab50","observation_id":"639ebb4f-649f-43fc-9712-1076997f84d1","resolution":{"observed_at":"2026-08-12T15:53:22.710463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.678630Z","title":"Wireless distributed edge learning: How many edge devices do we need?","venue":null,"work_id":"893a35ea-3433-4356-918f-6741319e5754","year":2020},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.605594Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:a20ff7518bbe7093438f88a88aa77ce517544d0fbe01bfe28d47416965dcddd4","observation_id":"caeeef9b-d061-42dc-ac48-ccaf83b29e56","resolution":{"observed_at":"2026-08-12T15:53:22.681986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:21.610151Z","title":"Scheduling policies for federated learning in wireless networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.610151Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:327cd829cb4d5d048d5318cf4af25c0ddeec04add7de69cd74941b8ab29aa854","observation_id":"47b2addb-61f5-4dbe-b7dc-74fb511ba074","resolution":{"observed_at":"2026-08-12T15:53:21.610151Z","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":"2026-08-12T15:53:22.665085Z","title":"Joint device selection and bandwidth allocation for cost-efficient federated learning in industrial internet of things,","venue":null,"work_id":"72acd838-eee9-45d6-9b2a-8123167e9fc9","year":2023},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.680104Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:5dd7e518c122423788c915ea07576ae476fab23b4a8b11b6d5d3a2a94b36674a","observation_id":"8c9480ed-4485-4e8f-8c33-9cfa991649e9","resolution":{"observed_at":"2026-08-12T15:53:22.667783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.656420Z","title":"Over-the-air aggregation-based federated learning in cache-enabled wireless edge networks,","venue":null,"work_id":"50b0958d-723e-49ed-9e4e-78692008da0e","year":2023},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.813837Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:000ae1318a25250f15f4c324924b3c276ebd56eb9eb48c930b6fde19fd5007d8","observation_id":"3f101084-24d6-49ad-9d9c-22fa83932f51","resolution":{"observed_at":"2026-08-12T15:53:22.659567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.645061Z","title":"Base station dataset-assisted broadband over-the-air aggregation for communication-efficient federated learning,","venue":null,"work_id":"098823e5-4976-49f8-83b7-481261379791","year":2023},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.885499Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:f4f70df446636de3f5b6a95553c8e4917d0a5570691c2b9a1308a254f0016591","observation_id":"88e1ca6d-abc7-47a7-91cc-44afb01a2865","resolution":{"observed_at":"2026-08-12T15:53:22.650622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.518387Z","title":"Latency minimization for tdma-based wireless federated learn- ing networks,","venue":null,"work_id":"4a8cdfde-d05e-4b14-b756-bb75b5b1890f","year":2024},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.888147Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:8162f3ddb685cf74da9b78db1f404a7a9701547342181743812c59f6561358a6","observation_id":"6f4c990c-b13e-49d8-8284-ecdd9cccd144","resolution":{"observed_at":"2026-08-12T15:53:22.563363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.507213Z","title":"Device scheduling for energy-efficient federated learning over wireless network based on tdma mode,","venue":null,"work_id":"552c9b32-ff74-48ba-916c-a6a46a81cc42","year":2020},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.891975Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:f730f4f928d87090760e9ad0655d4150c3994c6a3aa7e7826f1ef3cc21f58b43","observation_id":"c4347a95-2e39-4e31-a18c-0efe9db41a75","resolution":{"observed_at":"2026-08-12T15:53:22.511487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.498430Z","title":"Wireless quantized federated learning: A joint computation and communication design,","venue":null,"work_id":"d71c80e8-d7f3-4fcb-8329-08f1797a1366","year":2023},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.894878Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:a20d57fb44f3144b8eef3e8043902e4096af031ed22eb5ac64e758390e90f3ed","observation_id":"b47e95e2-e7d9-4fe2-bd44-2cf8eac7c95a","resolution":{"observed_at":"2026-08-12T15:53:22.501489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.481746Z","title":"Multi- channel aloha optimization for federated learning with multiple models,","venue":null,"work_id":"f4c2290c-3d5d-4e84-9cc2-144842236e2a","year":2022},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.897662Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:0b24f427f3b40cc0172ad2df714dca986490a401bb6190a0c1182329c82b06e3","observation_id":"5e065476-2146-4f7d-8c7f-d9dd21801c5f","resolution":{"observed_at":"2026-08-12T15:53:22.492788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.377744Z","title":"Adaptive federated learning with gradient compression in uplink noma,","venue":null,"work_id":"941f14a1-0811-43d9-a906-4f6a74a7dfa5","year":2020},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.901411Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:0b407d8d7470a75f64a7383c2fb0021401f1c6730f95ab6c99b4d6ac96196d49","observation_id":"1f574f38-ec41-4d02-b972-8c2484b92996","resolution":{"observed_at":"2026-08-12T15:53:22.440797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.331671Z","title":"Asynchronous decentralized parallel stochastic gradient descent,","venue":null,"work_id":"84b4c403-fe71-4610-86b8-5dc65254ea19","year":2018},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:21.948197Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:e17809c6146811087ede16c0709bd2b575861151ed021bfc6af00df7f44b8875","observation_id":"0b51a944-8730-4c12-a797-9d944af67cfb","resolution":{"observed_at":"2026-08-12T15:53:22.334814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.321635Z","title":"Sharper convergence guaran- tees for asynchronous sgd for distributed and federated learning,","venue":null,"work_id":"0f35703a-a22a-4496-a67f-b07ec4ecea10","year":2022},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:22.027306Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:420ff202da1dc50b2afd20d403947301b6c34adc650e6e1d84afc39e403d12de","observation_id":"f22dd139-799a-4dea-b99d-c9a554cb111b","resolution":{"observed_at":"2026-08-12T15:53:22.325524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.055402Z","title":"Asynchronous federated learning over wireless communication networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:22.055402Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:5e606b271f9c7f12f56038d8078efc4efdb95d088ec5839b28d0072191a262ac","observation_id":"789afc48-945e-4a72-97e4-2d2adc7b17b1","resolution":{"observed_at":"2026-08-12T15:53:22.055402Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:53:22.065544Z","title":"Scheduling and aggregation design for asynchronous federated learning over wireless networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:22.065544Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:c38135274be75d16bef1bf96f08f77d2a0cebe2d51abf91c6cd26e4c9a4be301","observation_id":"11cc4fc8-8705-49c5-a7e6-d5daa0dd5bf7","resolution":{"observed_at":"2026-08-12T15:53:22.065544Z","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":"2026-08-12T15:53:22.283049Z","title":"The MNIST database of handwritten digit images for machine learning research,","venue":null,"work_id":"2373cfe3-31e3-46f3-8ac5-cd09730daf38","year":2012},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:22.071174Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:22196a10b03ec888eb9027f34b2c0a44d294a1efdf5e549445cfae5e3e36d6b3","observation_id":"a52e84c8-eff0-45e2-b284-63f16cf557b4","resolution":{"observed_at":"2026-08-12T15:53:22.305022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T15:53:22.083057Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T15:53:22.083057Z"},"links":{"citing_paper":"/paper/2411.13861"},"observation_digest":"sha256:bdf0db10317727128b249fa98b58601758b1e58b8b7a05391819f59359aabe28","observation_id":"131b7ec8-160e-4b7f-9855-57b62c4daf2b","resolution":{"observed_at":"2026-08-12T15:53:22.083057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.13861","last_updated":"2024-11-21T05:42:35Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-12T21:56:46.422384Z","submitted_at":"2024-11-21T05:42:35Z","title":"Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":1,"verified_fuzzy":16},"total_outbound_references":24},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2411.13861."}