{"as_of":"2026-08-12T18:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e7f244ad239e0fdd5e0cd2fbec5fc7e70f0b1ff0ca62b86d62b86c4e56baa6a8","coverage":[{"denominator":72,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":72,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T23:45:18.409341Z","state":"measured"},{"denominator":72,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":72,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2412.19991/citation-record","integrity":"/paper/2412.19991/integrity","json":"/paper/2412.19991/citation-record.json","paper":"/paper/2412.19991"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:45:18.027313Z","title":"Deep learning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.027313Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:218426bfbd385bed027784c1a5275f537b4b235e550ea20ac607fa61fdb93b9f","observation_id":"311ff130-a810-46d9-8f99-e8104c7c7f3f","resolution":{"observed_at":"2026-08-10T23:45:18.027313Z","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":"2026-08-10T23:45:19.421564Z","title":"Natu- ral language processing: state of the art, current trends and challenges","venue":null,"work_id":"d4a57368-91b0-4057-b332-737bb46d3197","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.035135Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:9e81b44659c89cf397a2b0673b143ba8986b5ab80747f50d0b59daeac35ce1fa","observation_id":"0730a1a7-2219-4bbe-af1f-a48144d1ea56","resolution":{"observed_at":"2026-08-10T23:45:19.426129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.407993Z","title":"Dif- ferentially private image classification by learning priors from random processes","venue":null,"work_id":"7d34972f-5b85-43ce-b1c4-576fcc113cbd","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.040594Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:84c2dfdc049655ec557f4b37c7c7c7fca22ac3440b30c21d779c7f494200e058","observation_id":"7b02e21b-c24d-41db-9938-1f8077cf7946","resolution":{"observed_at":"2026-08-10T23:45:19.412678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.394361Z","title":"Federated learning for generalization, robustness, fairness: A survey and benchmark","venue":null,"work_id":"290fbfd9-8cb3-41fc-b3d5-2e27c53619a9","year":2024},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.047226Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:a27b8eb6dd106ace296f138328e13067a5653cb1a587e52f84b02a5ee1185b98","observation_id":"e8513ce7-ddbf-4410-95ab-6aee62794b40","resolution":{"observed_at":"2026-08-10T23:45:19.399548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.381253Z","title":"Vertical federated learning: Concepts, advances, and challenges","venue":null,"work_id":"98a1bbc2-a9c0-4148-b7d3-55d63da634d8","year":2024},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.052154Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:b42a00e0580d81859ad13481b15ff51fa25c480b166a70ec93917f6e8a2f7092","observation_id":"aaef6138-aec6-406e-a9d9-c0462ce76479","resolution":{"observed_at":"2026-08-10T23:45:19.385765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.366516Z","title":"Billion-scale federated learning on mobile clients: A submodel design with tunable privacy","venue":null,"work_id":"e4ca67ca-af27-4076-ab2c-85c70845b726","year":2020},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.057667Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:ec93f424801bf11e491a5db2968f48c81240c436e258b576704a47902bd702a6","observation_id":"3a07f3bc-c383-4f2a-9dac-97e2cc6f85b3","resolution":{"observed_at":"2026-08-10T23:45:19.372060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.350789Z","title":"Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges","venue":null,"work_id":"e8e35520-df00-44af-8960-5c108bfc20a0","year":2022},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.062936Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:44cf66cf8b24f8fa74eb7479a36fce24ed8ba0b3e8a328792abb745722841bc9","observation_id":"ae02e41d-1acb-4e3a-9a24-77f5029c8055","resolution":{"observed_at":"2026-08-10T23:45:19.355958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.068077Z","title":"Federated learning for smart healthcare: A survey","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.068077Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:c909398ef58d50266b365d42beb9d2c688791cc67903723dbe54adb1b0e9a0ad","observation_id":"c5fa47ab-2f10-4bb5-8b9d-bea9777925b0","resolution":{"observed_at":"2026-08-10T23:45:18.068077Z","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":"2026-08-10T23:45:19.326736Z","title":"Towards federated learning at scale: System design","venue":null,"work_id":"1313730d-6518-42a2-b23e-df072e29a970","year":2019},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.072772Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:5c1f73e0125ca91d0431d6cd76bfdf2dba6a148eca5bed3394011be2dad9adc2","observation_id":"64882276-50d0-473d-918d-2042c4529d4a","resolution":{"observed_at":"2026-08-10T23:45:19.331444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.077348Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.077348Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:a0f7ea2b353a2f8581f0674b432a27d5f475fb626122a71298f2a4db19aa25a1","observation_id":"68aaa86a-abc4-435b-be70-feb9ef8751f0","resolution":{"observed_at":"2026-08-10T23:45:18.077348Z","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":"2026-08-10T23:45:19.305155Z","title":"Safa: A semi-asynchronous protocol for fast federated learning with low overhead.IEEE Transactions on Computers, 70(5):655– 668, 2020","venue":null,"work_id":"4f9c6f1a-5bf4-4b74-878c-ec21142888f3","year":2020},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.082434Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:a43d70dd48db308428d94d07889f4602dceb987a172a6215dfceef2a537f78fe","observation_id":"fb7bfead-c772-4c36-9428-72e55cdef956","resolution":{"observed_at":"2026-08-10T23:45:19.309624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.291110Z","title":"Oort: Efficient federated learning via guided participant selection","venue":null,"work_id":"6a4082ae-3c15-47b3-9a06-16a6950ba518","year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.087738Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:b1058d8cf1e5c3a8aeabffd0f78fbdc41a733800aaf98448ff987c09f8fda923","observation_id":"9b37006e-5adc-4872-a600-5784c2370d94","resolution":{"observed_at":"2026-08-10T23:45:19.296039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.278340Z","title":"Hermes: an efficient federated learning framework for heterogeneous mobile clients","venue":null,"work_id":"f7126829-d3f4-4258-a748-75ae4409bbb3","year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.092553Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:135e19169f974c12796dad1dd2188d912347c8f1a4c2cb3fa1814ccd99b812fd","observation_id":"ac82b6a9-0c5e-43eb-89ee-8949c817587d","resolution":{"observed_at":"2026-08-10T23:45:19.282498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.264650Z","title":"Pyramidfl: A fine-grained client selection framework for efficient federated learning","venue":null,"work_id":"b8322342-3bb8-4588-a025-3b8caf910319","year":2022},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.097495Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:b48f78016bdc95e61d1cd74e988c5558b8f65ff2efbb8e64ec55f065b5232524","observation_id":"8db12d95-8130-41c5-bd72-c14f51849dbc","resolution":{"observed_at":"2026-08-10T23:45:19.269498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.250641Z","title":"Fedsea: A semi-asynchronous federated learning framework for extremely heterogeneous devices","venue":null,"work_id":"2f8ccbde-e0bd-4620-9eb9-98d65126e5f9","year":2022},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.102240Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:d2468f84f0c8684355ce8d9cc9a279b704efb6d649731218dcc04d2965b0e5da","observation_id":"441b75e4-b3b5-4918-a5c0-b46ebfce64e1","resolution":{"observed_at":"2026-08-10T23:45:19.255543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.13797","last_updated":"2022-06-07T03:48:55Z","snapshot_observed_at":"2026-07-06T13:14:33.576055Z","submitted_at":"2022-05-27T07:18:11Z","title":"AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.13797","snapshot_observed_at":"2026-08-10T23:45:18.107718Z","title":"Asyncfeded: Asynchronous federated learning with euclidean distance based adaptive weight aggregation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.107718Z"},"links":{"cited_paper":"/paper/2205.13797","citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:7d6fd6455f10c9ac929a159c27c8f69e09c46c6da9feab2a623c340f50881d03","observation_id":"2fbe50cd-5ca1-477a-9b9c-6c0a76742bed","resolution":{"observed_at":"2026-08-10T23:45:18.107718Z","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":"2026-08-10T23:45:19.236148Z","title":"Bose: Block-wise federated learning in het- erogeneous edge computing","venue":null,"work_id":"2ba94dbd-e079-4e99-921b-ee0537b42468","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.114133Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:f47b71e43e968c7cb0d45242e75be8786097b189eb3411b07f74add30ab884da","observation_id":"6f784a6b-e266-4d65-8a60-8101a94f8f7b","resolution":{"observed_at":"2026-08-10T23:45:19.241057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.222769Z","title":"Efficient federated learning for modern nlp","venue":null,"work_id":"775f90d6-b17e-4793-90bb-4881cabfd835","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.119465Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:cff9eab20258432722d409e01bf2319c9847110be8f1e9bf6cf92d9463325608","observation_id":"12398e1c-d7ba-4cbd-bd2d-b101c07d63cc","resolution":{"observed_at":"2026-08-10T23:45:19.227223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.208667Z","title":"Federated few-shot learning for mobile nlp","venue":null,"work_id":"b909b1db-93e3-41b9-9777-9e79c5077b0e","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.124474Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:ad0761225cf1cd376092ca321474ad6dccf586f778f9cbaa4a1775e4340c8322","observation_id":"96a656e7-823c-4156-a81e-5a218e1910cc","resolution":{"observed_at":"2026-08-10T23:45:19.213420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.194181Z","title":"Aut- ofed: Heterogeneity-aware federated multimodal learning for robust autonomous driving","venue":null,"work_id":"b3be0931-31cb-4a10-bd0b-a0b2810a9a1a","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.129644Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:672e88197f9799099a345c26a9a0b899765b8f8f77d4ef79395d3df92b03effc","observation_id":"b896f1c1-c98a-4ce1-b097-e27961baa2b6","resolution":{"observed_at":"2026-08-10T23:45:19.198718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.177977Z","title":"In 2024 USENIX Annual Technical Conference (USENIX ATC 24), pages 579–596, 2024","venue":null,"work_id":"6cd1e55a-06a2-498e-9435-e088fbcc8237","year":2024},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.136100Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:e3c355dc54c5b539f9ce75c080756bd6c30db8292b68ff4c4feeb92569a5db74","observation_id":"776dc1ca-c49b-4973-aa92-f458ca6b012f","resolution":{"observed_at":"2026-08-10T23:45:19.183422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.141262Z","title":"Federated learning with non-iid data","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.141262Z"},"links":{"cited_paper":"/paper/1806.00582","citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:ab121ef5259ad4b48dbe514355d1bd9065c099115ca2a256789bc9d6e361aa91","observation_id":"c3233229-5723-466a-8a89-23778a1cd776","resolution":{"observed_at":"2026-08-10T23:45:18.141262Z","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":"2026-08-10T23:45:18.147108Z","title":"Imagenet classification with deep convolutional neural networks","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.147108Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:90a58c3cfae43986fc39d2c68d2318e885b879f3879b7f57d7e10c7335cf1997","observation_id":"060c5746-9b55-4e57-8598-e08c179c3d03","resolution":{"observed_at":"2026-08-10T23:45:18.147108Z","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":"2026-08-10T23:45:18.153161Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.153161Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:e262dd2d86ffda294ca058f7cad598c387a56a33e39944bf4b3f541c2a2cd377","observation_id":"a2318e24-068e-4183-8f3b-d89d480a2e9b","resolution":{"observed_at":"2026-08-10T23:45:18.153161Z","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":"2026-08-10T23:45:19.145801Z","title":"Multitude of beta distributions with applications","venue":null,"work_id":"24529b32-77b5-466e-a944-2f172db7708f","year":2007},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.159470Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:ec1943430d3633a4c20c1fe09e3d7b4eafccb3835d126e3e83f5b7fe51257a3d","observation_id":"905b13d5-d0d5-49ca-ae16-387f90e6126d","resolution":{"observed_at":"2026-08-10T23:45:19.150484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.164629Z","title":"Bayesian theory, volume 405","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.164629Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:c072d95ac3526bf068f36eab599bc6007f8ef73997d8d7cd52996bd2d4720b6c","observation_id":"4c9c07da-a5b2-4344-af4e-4e10625e44cb","resolution":{"observed_at":"2026-08-10T23:45:18.164629Z","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":"2026-08-10T23:45:18.171620Z","title":"Finite-time analysis of the multiarmed bandit problem","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.171620Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:25a780d0eff199ea4ffd876758e1b4d6368fa5a9b592f5bf2ca4314998af3898","observation_id":"06cb872d-0312-49a5-bd86-48cb0c46ac98","resolution":{"observed_at":"2026-08-10T23:45:18.171620Z","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":"2026-08-10T23:45:19.110542Z","title":"Towards efficient and stable k-asynchronous federated learning with unbounded stale gradients on non-iid data","venue":null,"work_id":"b064c0e6-13ce-4620-8200-3e8c03b87c32","year":2022},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.177475Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:5eb9522850cbaf6261df6742f52ae81144970f1c2bcba7027c8643dea144cc42","observation_id":"3a5273c8-6a3a-443b-bc7d-214ca62dcfba","resolution":{"observed_at":"2026-08-10T23:45:19.116121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.097265Z","title":"Fleet: Online federated learning via staleness awareness and performance prediction","venue":null,"work_id":"8881472a-0f95-467a-b625-1fe6ece257b7","year":2022},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.182520Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:a6bc6b6ba9176b734699002dfd2b875249e531af1ed717410bfbf4f1eb9c8b25","observation_id":"5e869283-5032-4421-8cdb-e29681cb13b1","resolution":{"observed_at":"2026-08-10T23:45:19.102254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.083705Z","title":"Fedpa: An adaptively partial model aggregation strategy in federated learning","venue":null,"work_id":"e0d60672-cc74-4b8d-88b8-5202cbee7b8c","year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.190378Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:388bc08bb468c23ffac0becb720f2fd8e069501033b358d60a466debb27e3de6","observation_id":"17935819-2796-428a-9db0-f68ee7da8d0a","resolution":{"observed_at":"2026-08-10T23:45:19.088939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.070479Z","title":"Robust asynchronous federated learning with time-weighted and stale model aggregation","venue":null,"work_id":"0ed834a9-14ae-4f99-9c77-bb8b2432375c","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.196338Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:8afa50ace01e699fb927d37cecbab4aabe5eb5f715a2fc226ab8c6eaec97fea4","observation_id":"95a14bd7-a2c1-4c0d-aa08-db9f8dfb747a","resolution":{"observed_at":"2026-08-10T23:45:19.075312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.057262Z","title":"Hiflash: Communication-efficient hi- erarchical federated learning with adaptive staleness control and heterogeneity-aware client-edge association","venue":null,"work_id":"dc6707c0-94b6-4fca-9ac8-228bb2350a5f","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.204458Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:60c668f2999bf011a5ed7f080f916a32e871cd3ef622d80b3487bc5d6ce51701","observation_id":"96deab17-0b2c-45bf-962d-f3f06320725a","resolution":{"observed_at":"2026-08-10T23:45:19.061808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.043407Z","title":"https://www.oppo.com/en/smartphones/","venue":null,"work_id":"7dfd6274-9932-45ed-978a-b7625eadeddf","year":null},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.211284Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:9245ed149528f1cd55f8faa810e0811310eb335b508218be706f49218a35e3ca","observation_id":"e1cda87f-6d11-4bd6-b872-6201554f1176","resolution":{"observed_at":"2026-08-10T23:45:19.047838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.028268Z","title":"https://github.com/alibaba/MNN?tab=readme-ov-file","venue":null,"work_id":"c2f1bf5d-83e6-4e4e-8773-f8182b0e880f","year":null},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.216893Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:6c29d6b9b27302ab43ab3c5d16c64af96a5a470c5e007ce1ffee14fdd7b86177","observation_id":"2d8477ee-d97a-4b46-8ad8-e90d51e4614a","resolution":{"observed_at":"2026-08-10T23:45:19.033170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:19.012433Z","title":"https://docs.nvidia.com/jetson/","venue":null,"work_id":"813f7444-49ee-404e-9336-f9e84025fe92","year":null},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.223520Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:621a9d23fc55c7359bac56c1b6fda5ac3606b369438d8415c515687bf34eea73","observation_id":"fee3bcbc-21da-444c-be4f-2b7399e17403","resolution":{"observed_at":"2026-08-10T23:45:19.017925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.990096Z","title":"Docker: lightweight linux containers for consistent development and deployment","venue":null,"work_id":"0be01994-d615-4472-ba84-43c12305be8b","year":2014},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.229651Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:4c82f7d6ee4e7e75d4b44dc7446b2adfd242d2e68156db979626aa1564fefac1","observation_id":"de414f07-e8fc-438b-80f5-74fd5495daa1","resolution":{"observed_at":"2026-08-10T23:45:18.995634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.973075Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":"3baa7e09-a0bf-4247-b33c-018695cb23c1","year":2019},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.234931Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:486ace7e048a28581b80554d772fb04fd7a7a740f950b4585be3879b57ce7c3e","observation_id":"b288033c-8ee0-446b-811d-cb5f53ee16c6","resolution":{"observed_at":"2026-08-10T23:45:18.978771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.957958Z","title":"http://dast.nlanr.net/ Projects/Iperf/","venue":null,"work_id":"2c8cdebc-ef88-4e9c-a9a6-f1080dd1a47b","year":null},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.239885Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:381d82372fd5da0696a1eeee10d53dff004cba7462c4754baaecb4df9672a9a7","observation_id":"b6d82fa8-7939-4bf5-94fc-e027510c3286","resolution":{"observed_at":"2026-08-10T23:45:18.962633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.941443Z","title":"Deep convolutional neural net- works for image classification: A comprehensive review","venue":null,"work_id":"79b43b0b-0486-4f52-833f-850aa8c26170","year":2017},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.244701Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:2adbabe4fc348f4b462a894e37f549aab84b9fb3a1676c4ab05b04b1c4993014","observation_id":"8510cf30-15c9-4e77-981f-01313f597f9d","resolution":{"observed_at":"2026-08-10T23:45:18.947673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.920983Z","title":"https://github.com/ymliao98/PS_socket/blob/main/models.py","venue":null,"work_id":"7646375d-3963-4f13-a3c4-69cb716ca750","year":null},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.249748Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:6b590f8a3bca780e63001253ab04359eb316681cf41f2243843766f65b7d3af4","observation_id":"f4db4cbe-2e88-4cf1-8530-e532eefc715b","resolution":{"observed_at":"2026-08-10T23:45:18.925790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.255173Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.255173Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:02554894311ced9a9abbd70c7717c5e781385ffe5668decd32e568077ddfeaf5","observation_id":"95daaf77-8729-49b6-b2aa-51db5051f369","resolution":{"observed_at":"2026-08-10T23:45:18.255173Z","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":"2026-08-10T23:45:18.895787Z","title":"Biosignal sensors and deep learning-based speech recognition: A review","venue":null,"work_id":"3be852e0-e102-4489-aa78-5ea5264fa246","year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.261893Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:54c13fed9225b0c0d2c2ded77f67e04f7f3a5583d2d3d5d7c0317427a097dbf9","observation_id":"6fefb785-e074-4657-9356-932f5a11a0ff","resolution":{"observed_at":"2026-08-10T23:45:18.900563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.881157Z","title":"https://pytorch","venue":null,"work_id":"5db06653-c3b8-4cb3-b2be-1b0315e90c57","year":null},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.266463Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:4309cb48a26cfaf4d45c1d83507d33df99d411e65c48b5dbf833bf9d92ce396f","observation_id":"8fbc40d1-f093-4bcb-aa94-cb72755efa2b","resolution":{"observed_at":"2026-08-10T23:45:18.885336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.03209","last_updated":"2018-04-09T19:58:17Z","snapshot_observed_at":"2026-07-06T06:32:32.083176Z","submitted_at":"2018-04-09T19:58:17Z","title":"Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.03209","snapshot_observed_at":"2026-08-10T23:45:18.272870Z","title":"Speech commands: A dataset for limited-vocabulary speech recognition","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.272870Z"},"links":{"cited_paper":"/paper/1804.03209","citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:f80ce9b2c2fa8c2d6fbc12f7956c434396f43a8dbc731fe7ee23ad64cdd43c0d","observation_id":"933955a6-eda6-461c-b6f2-fe3ed75ca3db","resolution":{"observed_at":"2026-08-10T23:45:18.272870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.11441","last_updated":"2025-08-06T03:23:30Z","snapshot_observed_at":"2026-08-11T01:49:50.718341Z","submitted_at":"2024-01-21T09:42:24Z","title":"On-Device Recommender Systems: A Comprehensive Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.11441","snapshot_observed_at":"2026-08-10T23:45:18.279055Z","title":"On-device recommender systems: A comprehensive survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.279055Z"},"links":{"cited_paper":"/paper/2401.11441","citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:b80470867045630307097ce49c136d0507f9ee614d34a220fb8d1250f25cbca6","observation_id":"8bb7fd0c-fc15-4fbf-945b-4c5235845da2","resolution":{"observed_at":"2026-08-10T23:45:18.279055Z","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":"2026-08-10T23:45:18.285642Z","title":"Wide & deep learning for recommender systems","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.285642Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:25e81c4767f94cb13b511ce5ae2b1ae930d3b30f8eda2d01d33bdc380a590877","observation_id":"9c1ab94a-4956-4e28-9896-d12886c91cba","resolution":{"observed_at":"2026-08-10T23:45:18.285642Z","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":"2026-08-10T23:45:18.856958Z","title":"https://www.kaggle.com/c/avazu-ctr-prediction/ data","venue":null,"work_id":"24df8d64-842c-4439-aaeb-378166464b8b","year":null},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.290557Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:1ea67f0e9c11fb005f15426ed033ca98cd19152a139ef8706c48a789e72994d6","observation_id":"fc138079-1b71-4ffd-bedf-e0d64606a38d","resolution":{"observed_at":"2026-08-10T23:45:18.861564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.842138Z","title":"Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges","venue":null,"work_id":"80b6b555-b16f-4bc0-aaa4-b0772ab129e7","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.297306Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:c54f731cf267b153a25a1318b1f182322618a95028d1439a4759290d1b6a0149","observation_id":"4493bee5-8049-4606-8e7e-43458a507e4d","resolution":{"observed_at":"2026-08-10T23:45:18.847014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.826842Z","title":"Fedlab: A flexible federated learning framework","venue":null,"work_id":"f6b56d85-22dd-444b-8d8c-05f08591fed2","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.303546Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:47ae3944d445067b0f7a20bb0a59c7ab0de53bcea469fa5f5f83756faf8a6e15","observation_id":"80792de5-67e4-4504-bc7a-d68fa99f48a7","resolution":{"observed_at":"2026-08-10T23:45:18.831642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.811913Z","title":"Federated learning for internet of things: Recent advances, taxonomy, and open challenges","venue":null,"work_id":"eca5bd81-60ea-43ce-a21f-77d1fb520832","year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.308450Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:433bb14c982b8da659b5b80864d91216e75aa3e5f01cd371edd93605d5d005cc","observation_id":"c4aa9cd4-0f04-4235-98c3-2eda981fad21","resolution":{"observed_at":"2026-08-10T23:45:18.816486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.05428","last_updated":"2021-01-14T02:44:28Z","snapshot_observed_at":"2026-08-12T05:26:09.287528Z","submitted_at":"2021-01-14T02:44:28Z","title":"Federated Learning: Opportunities and Challenges","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.05428","snapshot_observed_at":"2026-08-10T23:45:18.312857Z","title":"Federated learning: Opportunities and challenges","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.312857Z"},"links":{"cited_paper":"/paper/2101.05428","citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:09de47e94b1f8ffe9f33f95a3cf5df00169531dab68fea57c614d8c0c9cca25f","observation_id":"d8613ac0-3d0d-43ac-84a0-ccffbf29be65","resolution":{"observed_at":"2026-08-10T23:45:18.312857Z","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":"2026-08-10T23:45:18.317639Z","title":"Federated optimization in heterogeneous networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.317639Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:1f56597a3e5d7898f2ce32bc18c225a1f141f3cdcddb37dfddcd659e934d69d1","observation_id":"d64fbcea-c5aa-4e21-8a7b-08fc6cf2f7c5","resolution":{"observed_at":"2026-08-10T23:45:18.317639Z","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":"2026-08-10T23:45:18.786992Z","title":"Adaptive federated optimization","venue":null,"work_id":"9841e727-bb73-4c96-9b4f-325bb8634ca4","year":null},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.321765Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:424e2192ddf87ea4e62408fb8bab72e74425554e2f917ffa312013f04c03d8e5","observation_id":"3000714c-b2e1-48ba-998b-28a7d8e52a5d","resolution":{"observed_at":"2026-08-10T23:45:18.792914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.773277Z","title":"Fedur: Federated learning optimization through adaptive centralized learning optimizers","venue":null,"work_id":"af0e4ee5-7320-480b-9abe-616b39d1be44","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.326529Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:511b5d5a36d662ccf23b83db259b86bf3f710a23bb0b828e01f6833abfeccf46","observation_id":"a96e38d7-5d21-4bd5-9c2e-e2d6b1f77891","resolution":{"observed_at":"2026-08-10T23:45:18.777924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.759351Z","title":"Fedala: Adaptive local aggregation for person- alized federated learning","venue":null,"work_id":"d1cbfacf-e0ca-4db9-9dc3-b2dc2990b5c6","year":2023},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.330543Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:96f9f01935c36a0bd1daf6069458cd3f3f08b770b02559ccb8f6ba25f6df3a75","observation_id":"d53eddea-fe2e-427b-8983-5eb5509284f4","resolution":{"observed_at":"2026-08-10T23:45:18.763943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.335160Z","title":"Towards personalized federated learning via heterogeneous model reassembly","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.335160Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:fbf13aed32e380a57e1049336a5d1a56e7511fc21271a4107334ead5adec00f1","observation_id":"22b4416a-0b0b-403f-a7fa-c5a1fdedc209","resolution":{"observed_at":"2026-08-10T23:45:18.335160Z","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":"2026-08-10T23:45:18.736164Z","title":"Flow: per-instance personalized federated learning","venue":null,"work_id":"4e92d949-d721-4d87-8248-c438530388a4","year":2024},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.339549Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:4c94cfedaca6dddde127900c386ec004858adaf9f705b32d536f7498bcc9fd61","observation_id":"bc4ea047-0f9c-4e58-a8bc-fa64a8c921f3","resolution":{"observed_at":"2026-08-10T23:45:18.740685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.721885Z","title":"Fedas: Bridging inconsis- tency in personalized federated learning","venue":null,"work_id":"0342cfff-3947-4b65-8b3e-81bbb905db3c","year":2024},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.343809Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:9367fe261269f76dd08a6bc0b338915fa09d749ea011d7b6d141580c9e3d8993","observation_id":"4e2bb511-3c63-4240-8b9e-aeddc5c67e7c","resolution":{"observed_at":"2026-08-10T23:45:18.727175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.10996","last_updated":"2021-07-23T02:13:11Z","snapshot_observed_at":"2026-07-06T11:31:46.316473Z","submitted_at":"2021-07-23T02:13:11Z","title":"Communication Efficiency in Federated Learning: Achievements and Challenges","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.10996","snapshot_observed_at":"2026-08-10T23:45:18.348131Z","title":"Communication efficiency in federated learning: Achievements and challenges","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.348131Z"},"links":{"cited_paper":"/paper/2107.10996","citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:81ade57947a41f2655c126aa9dc2e5c9ee1f6870ba84642076af199916803389","observation_id":"0586f9c2-f22a-4b27-8dd6-c1a071787a65","resolution":{"observed_at":"2026-08-10T23:45:18.348131Z","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":"2026-08-10T23:45:18.708216Z","title":"Uveqfed: Universal vector quantization for federated learning","venue":null,"work_id":"b5d83cfe-211a-4754-978f-188d7457e125","year":2020},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.353274Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:9367808ba47cd651d40701d4133fca1ae1b82c2404c6c2d3741530bbefbaf7b0","observation_id":"1bd651a4-6dc5-4965-8d58-322b33f72b5b","resolution":{"observed_at":"2026-08-10T23:45:18.712736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.693377Z","title":"Adaptive quantization of model updates for communication- efficient federated learning","venue":null,"work_id":"370fe963-44b4-42ac-8b20-c917067244c6","year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.358432Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:b40f49f2f7690e8f6d1c360e700eafe7e293bb707ba00155b7836d9fed2945d1","observation_id":"4e21fe0f-911e-4c03-84b0-a14281684dbe","resolution":{"observed_at":"2026-08-10T23:45:18.698749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.680083Z","title":"Towards mitigating device heterogeneity in federated learning via adaptive model quantization","venue":null,"work_id":"baca17bc-fd5d-47c6-9566-155391b43e51","year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.363296Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:f1cf927b9c86cd7b1754d37577c1d4e28a8eb1c2df4256abec234c38f24c7bc7","observation_id":"964420aa-40d6-4230-9d7d-a106c45f775a","resolution":{"observed_at":"2026-08-10T23:45:18.684661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.667440Z","title":"Neural network quantization in federated learn- ing at the edge","venue":null,"work_id":"28118822-70ac-4495-8ad2-0b5352d44cf8","year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.368439Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:2b1ac5cb7e6e4f835548defd42df6bfed5aab74fd0dfdc0ae6a4e9ed0b10d65c","observation_id":"d572a2d2-a613-40a2-bd49-af3b9d8ff8f5","resolution":{"observed_at":"2026-08-10T23:45:18.671735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10672","last_updated":"2020-10-07T01:15:06Z","snapshot_observed_at":"2026-08-09T02:11:01.681294Z","submitted_at":"2020-06-18T16:55:20Z","title":"Federated Learning With Quantized Global Model Updates","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10672","snapshot_observed_at":"2026-08-10T23:45:18.372942Z","title":"Federated learning with quantized global model updates","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.372942Z"},"links":{"cited_paper":"/paper/2006.10672","citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:e820819ae510e5b32161f821d20f6b48720d1da242469a83118c3df0ed98b8d5","observation_id":"7d3f97e0-38c5-42b7-9fd6-12eb932acddc","resolution":{"observed_at":"2026-08-10T23:45:18.372942Z","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":"2026-08-10T23:45:18.653391Z","title":"Model compression for communication efficient federated learning","venue":null,"work_id":"d761ee55-ad42-4da9-b03c-1516a6b525ed","year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.377750Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:c6b9e6159a35e2144164373823fcac27ee2c9ad8202d50d3efab65a0061192ca","observation_id":"03e1ae57-4957-422a-a0a1-ceff8f318976","resolution":{"observed_at":"2026-08-10T23:45:18.657710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.01593","last_updated":"2021-02-02T16:33:44Z","snapshot_observed_at":"2026-08-10T01:14:24.700555Z","submitted_at":"2021-02-02T16:33:44Z","title":"FEDZIP: A Compression Framework for Communication-Efficient Federated Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.01593","snapshot_observed_at":"2026-08-10T23:45:18.382400Z","title":"Fedzip: A compression framework for communication- efficient federated learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.382400Z"},"links":{"cited_paper":"/paper/2102.01593","citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:0cf945ced67889b0ceefb6de4d83ffcbde1035543f94cc312647e3b02981a5b4","observation_id":"51785202-a669-4204-b975-24203f34212f","resolution":{"observed_at":"2026-08-10T23:45:18.382400Z","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":"2026-08-10T23:45:18.638620Z","title":null,"venue":null,"work_id":"281e5e2b-ebb1-4b4b-970f-1d5af41f6557","year":2024},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.387151Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:4e91ca707b5177e14e6c4d15c792b0d2115a5820d761f76873a25db99bc06fc6","observation_id":"7bd0ddbe-9573-40d8-bc07-29e8097d20ce","resolution":{"observed_at":"2026-08-10T23:45:18.644756Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.03581","last_updated":"2019-10-08T18:00:00Z","snapshot_observed_at":"2026-08-12T16:52:56.503034Z","submitted_at":"2019-10-08T18:00:00Z","title":"FedMD: Heterogenous Federated Learning via Model Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.03581","snapshot_observed_at":"2026-08-10T23:45:18.391443Z","title":"Fedmd: Heterogenous federated learning via model distillation","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.391443Z"},"links":{"cited_paper":"/paper/1910.03581","citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:3c086a96d66137a484373b5f7467c552db014076c1fbbb382d7d0b1206b7585a","observation_id":"6c63ba58-4a1d-442f-9e83-2da6d765a755","resolution":{"observed_at":"2026-08-10T23:45:18.391443Z","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":"2026-08-10T23:45:18.624837Z","title":"Data-free knowledge distillation for heterogeneous federated learning","venue":null,"work_id":"16a1586c-e176-46c4-b173-71608395759c","year":2021},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.396340Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:88df27c03ef1ac7c846530b7e495b3a386c483bf8b71b0efbad7ea64f06f4d38","observation_id":"14e92361-d43f-46c8-86af-35d11d23e076","resolution":{"observed_at":"2026-08-10T23:45:18.629266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.401061Z","title":"Communication-efficient federated learning via knowledge distil- lation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.401061Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:de699e23c8cbe17b15232e083e392780b891b1682a28020ed8186279d68b28f1","observation_id":"99050970-9bd1-4457-b3ba-fd749ceb387a","resolution":{"observed_at":"2026-08-10T23:45:18.401061Z","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":"2026-08-10T23:45:18.604226Z","title":"Fedfed: Feature distillation against data heterogeneity in federated learning","venue":null,"work_id":"31c37236-fb4f-48eb-adfa-58fdbf2882c8","year":2024},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.405130Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:5ccc7f4e78c080e73aaba55724e9d3d198ef92a4c3c37df4014bdc6ad41d09fb","observation_id":"59ed01ad-2324-409e-94a6-a90c825931d1","resolution":{"observed_at":"2026-08-10T23:45:18.608531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T23:45:18.586761Z","title":"Dfrd: Data-free robustness distillation for heterogeneous federated learning","venue":null,"work_id":"7415d941-a5ef-40f3-b9e7-04c5cc1bfbc6","year":2024},"citing_paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-10T23:45:18.409341Z"},"links":{"citing_paper":"/paper/2412.19991"},"observation_digest":"sha256:fae190d0c77a8a8adc988800d5e9398a8b02a6612bf53be1e0b628cc6d45640b","observation_id":"424f6b5a-f821-4b79-b62c-1dbbc30aa320","resolution":{"observed_at":"2026-08-10T23:45:18.594050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.19991","last_updated":"2024-12-28T03:28:52Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T23:39:04.212924Z","submitted_at":"2024-12-28T03:28:52Z","title":"A Robust Federated Learning Framework for Undependable Devices at Scale"},"reference_resolution":{"displayed":72,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":0,"verified_fuzzy":50},"total_outbound_references":72},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 0 inbound Pith citation observations for arXiv:2412.19991."}