{"as_of":"2026-08-09T12:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f05638a84e366b70a521daba32527aa394fd04ea68b6ace200a168458380c527","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:15:29.794243Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2505.19605/citation-record","integrity":"/paper/2505.19605/integrity","json":"/paper/2505.19605/citation-record.json","paper":"/paper/2505.19605"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:15:32.764144Z","title":"The kuramoto model: A simple paradigm for synchronization phenomena.Reviews of modern physics, 77(1):137–185, 2005","venue":null,"work_id":"30e151fe-9ab7-4741-a759-03ca17a1d346","year":2005},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:26.812682Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:872be6ee762e55d1c551b1c1a40709360e22d8ca03217736b315b7ce9e67ff96","observation_id":"857a9b98-f88a-4b6c-982a-e740b0e91341","resolution":{"observed_at":"2026-08-07T14:15:32.810137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:32.629673Z","title":"Syn- chronization in complex networks.Physics reports, 469(3):93–153, 2008","venue":null,"work_id":"c1d6682b-cf13-417d-80d1-9a7ed5b01814","year":2008},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:26.919671Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:9cb93ab65682f29e5af935a922983ebcfafdae37fd715b3706dedac67f95b373","observation_id":"0e809983-f09c-472e-84b7-f75ee79de53c","resolution":{"observed_at":"2026-08-07T14:15:32.696720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:27.068672Z","title":"Emnist: Extending mnist to handwritten letters","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:27.068672Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:371d7afb3716f315c7c5308d6b687da68033aa362d1754a73f2f526d1ca2def9","observation_id":"f2047322-f844-483d-a22d-4a56d6240b95","resolution":{"observed_at":"2026-08-07T14:15:27.068672Z","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":"2026-08-07T14:15:32.493668Z","title":"Kuramoto model with frequency- degree correlations on complex networks.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 87(3):032106, 2013","venue":null,"work_id":"6a9d8b2c-b058-45f7-8203-f92668c3e177","year":2013},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:27.201375Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:995fa86861d57ce3442c449b1f3632e45ee41889b879c72a8bbd2b0c2c617c8c","observation_id":"e1b5e723-0f6a-4bc4-9fb6-613750277889","resolution":{"observed_at":"2026-08-07T14:15:32.542086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:32.312470Z","title":"Amplitude expansions for instabilities in populations of globally-coupled oscillators.Journal of statistical physics, 74:1047–1084, 1994","venue":null,"work_id":"01b6b80a-a4e5-4536-b54b-0b073ec5b84d","year":1994},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:27.308620Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:081ca064db29711d920bc43c2a259c844b85ecb08d71a60b9375ebc2d9da8687","observation_id":"7554e134-6579-4cc4-b377-f99300a69ed6","resolution":{"observed_at":"2026-08-07T14:15:32.364273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:32.177169Z","title":"Synchronization and transient stability in power networks and nonuniform kuramoto oscillators.SIAM Journal on Control and Optimization, 50(3):1616– 1642, 2012","venue":null,"work_id":"54f45b86-f519-4518-b61d-168c744e90e3","year":2012},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:27.445939Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:d8b630984328eab98d259b16393b59646b38602bfd891267c267a5c11a862f25","observation_id":"0b7d2e83-fe40-4425-b097-4c81c7120005","resolution":{"observed_at":"2026-08-07T14:15:32.227289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:32.020693Z","title":"Novel insights into lossless ac and dc power flow","venue":null,"work_id":"151dab01-4d0e-44ba-9746-500743d088df","year":2013},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:27.500461Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:af083c85a6e1b9011516c9d3517aaabecd30743210039c47897332780f9859be","observation_id":"ffff6662-1d7f-4e1e-bd31-edfe16df0716","resolution":{"observed_at":"2026-08-07T14:15:32.098227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:31.880499Z","title":"An adaptive model for synchrony in the firefly pteroptyx malaccae.Journal of Mathematical Biology, 29(6):571–585, 1991","venue":null,"work_id":"d301e2b3-3c86-4c55-b036-4da42a6a44d0","year":1991},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:27.573021Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:2fc8b2aa82db922f4e7b41a75bfb674dbdfcf0dbf27faaafa559808ef2b9f82f","observation_id":"0fc0549a-8388-48d6-8801-7c6e512bc642","resolution":{"observed_at":"2026-08-07T14:15:31.923538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:27.641450Z","title":"Scaffold: Stochastic controlled averaging for federated learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:27.641450Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:36440bd0dff407e51d5704301d641aca8465049d6c467c07341660aa341b85a2","observation_id":"167678b3-d1ef-4093-a623-f482c3c3b06f","resolution":{"observed_at":"2026-08-07T14:15:27.641450Z","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":"2026-08-07T14:15:31.724271Z","title":"Self-entrainment of a population of coupled non-linear oscillators","venue":null,"work_id":"f83433bd-3d33-4534-9b2f-22b7e1a470f6","year":1975},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:27.691259Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:8cc8f5e9a9efc6d5ec2fd76d412ceb4ba4b9e90affca43cac9c0f260b9879c48","observation_id":"a602792d-93c6-499a-a817-f2b384078327","resolution":{"observed_at":"2026-08-07T14:15:31.771442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:27.748536Z","title":"Federated optimization in heterogeneous networks.Proceedings of Machine learning and systems, 2:429–450, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:27.748536Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:a6648f5e8a2e1193c5cd03ae4eaf75f79443dfeca708e4e40ff1ef00c6938d0f","observation_id":"f532bf49-3da1-491d-93ed-2b27dd22a085","resolution":{"observed_at":"2026-08-07T14:15:27.748536Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.07623","last_updated":"2021-05-11T14:21:00Z","snapshot_observed_at":"2026-07-06T10:41:31.563193Z","submitted_at":"2021-02-15T16:04:10Z","title":"FedBN: Federated Learning on Non-IID Features via Local Batch Normalization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.07623","snapshot_observed_at":"2026-08-07T14:15:27.807675Z","title":"Fedbn: Federated learning on non-iid features via local batch normalization.arXiv preprint arXiv:2102.07623, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:27.807675Z"},"links":{"cited_paper":"/paper/2102.07623","citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:979f210d37432cb12f2b6b9792f1d282794ac548f50fbc2fe37e1596028a8cd6","observation_id":"51c6317f-92c7-4a99-b284-3331b21d7304","resolution":{"observed_at":"2026-08-07T14:15:27.807675Z","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":"2026-08-07T14:15:31.554342Z","title":"Synchronization in the random- field kuramoto model on complex networks.Physical Review E, 94(1):012308, 2016","venue":null,"work_id":"dcdd2b14-d4c1-460f-9e27-1b6f3d40cf04","year":2016},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:27.896338Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:7673350be19e9b3adde2852d8eb7257d5e8bbf3b767d2a87cbbc6076362a4d6d","observation_id":"82cb91e8-d841-4940-beba-c9701f17e42c","resolution":{"observed_at":"2026-08-07T14:15:31.628256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:28.000867Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:28.000867Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:4741dee5e35078c832ed229bb2875f1a3d8f1bf20d31c94858c325c2f56189b7","observation_id":"85d638ba-e531-4b4e-b584-7cf845d7dd56","resolution":{"observed_at":"2026-08-07T14:15:28.000867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.08458","last_updated":"2015-12-02T18:06:03Z","snapshot_observed_at":"2026-07-06T04:37:52.737823Z","submitted_at":"2015-11-26T17:45:01Z","title":"An Introduction to Convolutional Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.08458","snapshot_observed_at":"2026-08-07T14:15:28.117826Z","title":"An introduction to convolutional neural networks.arXiv preprint arXiv:1511.08458, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:28.117826Z"},"links":{"cited_paper":"/paper/1511.08458","citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:5652541c9a4bc842e54a9fcbab7220d3e759b814aa1395276928d7e8f4febc5b","observation_id":"42937406-698a-46af-887d-e1fc26e22450","resolution":{"observed_at":"2026-08-07T14:15:28.117826Z","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":"2026-08-07T14:15:31.393226Z","title":"Network dynamics of coupled oscillators and phase reduction techniques.Physics Reports, 819:1–105, 2019","venue":null,"work_id":"54d2e631-5d87-44f7-aa95-c74582b6bfaf","year":2019},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:28.268817Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:1963c8f449be93d273c4e6dfbc18abe433f802235d8ccd9e36efb3b897ab7667","observation_id":"21098513-5c0e-4d36-898a-4a17f312ae57","resolution":{"observed_at":"2026-08-07T14:15:31.443826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00451","last_updated":"2018-06-01T17:16:56Z","snapshot_observed_at":"2026-08-06T16:44:27.229416Z","submitted_at":"2018-06-01T17:16:56Z","title":"Do CIFAR-10 Classifiers Generalize to CIFAR-10?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.00451","snapshot_observed_at":"2026-08-07T14:15:28.384676Z","title":"Do cifar-10 classifiers generalize to cifar-10?arXiv preprint arXiv:1806.00451, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:28.384676Z"},"links":{"cited_paper":"/paper/1806.00451","citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:68691cdbeacf1e7a6c9b768c3acd393df5a5c9c0fefc23193523e868bec5d787","observation_id":"4d74f531-bed8-47e4-a592-4d10284c8793","resolution":{"observed_at":"2026-08-07T14:15:28.384676Z","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":"2026-08-07T14:15:31.243848Z","title":"The kuramoto model in complex networks.Physics Reports, 610:1–98, 2016","venue":null,"work_id":"f4772f48-5b70-4d47-bd69-3ed876c1d815","year":2016},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:28.531884Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:54be5a727e8656ed7dc292ca7c99aa793dce4930371d04cd99a5ad19275a2cdb","observation_id":"aafb3c1f-5a28-467f-8aa4-ebaefeaa97ef","resolution":{"observed_at":"2026-08-07T14:15:31.321933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00182","last_updated":"2025-06-03T16:38:42Z","snapshot_observed_at":"2026-07-06T20:29:21.568793Z","submitted_at":"2025-01-31T21:58:15Z","title":"Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study","version":3},"cited_work":{"arxiv_id":"2502.00182","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.00182","snapshot_observed_at":"2026-08-07T14:15:30.051679Z","title":"Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study","venue":"cs.LG","work_id":"986846c6-e008-4079-8775-3e6d98492462","year":2025},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:28.633807Z"},"links":{"cited_paper":"/paper/2502.00182","citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:ce4f480709cd4ddcaeb67fe0bfa8b5296d5fbc2a5c606d14c6a6b1352d6ff463","observation_id":"fcee1be4-4204-42fb-b7cb-65e397b49626","resolution":{"observed_at":"2026-08-07T14:15:30.104695Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:31.104390Z","title":"Higher order interactions in complex networks of phase oscillators promote abrupt synchronization switching.Communications Physics, 3(1):218, 2020","venue":null,"work_id":"abec996f-f378-474d-8656-9d8c70399764","year":2020},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:28.784611Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:fd2884c63edf21e0d7ec0b81acbf9d22b2607829cf8eadaed00fbdda9b856803","observation_id":"8d8c2595-3632-4df2-8e0c-790201cc8e39","resolution":{"observed_at":"2026-08-07T14:15:31.179944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:30.984290Z","title":"Sync: The emerging science of spontaneous order","venue":null,"work_id":"6553dd92-99a4-4d68-a680-8576a3e76a8e","year":2004},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:28.922716Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:b6c09744e8a1dc68a35a67f8c00b33a23b5e0d14b9ea14a75ae8e3b443e2d1dc","observation_id":"124f1d25-7f55-4a5a-826d-812d7e5bd3b5","resolution":{"observed_at":"2026-08-07T14:15:31.021472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:30.836905Z","title":"From kuramoto to crawford: exploring the onset of synchronization in populations of coupled oscillators.Physica D: Nonlinear Phenomena, 143(1-4):1–20, 2000","venue":null,"work_id":"3e7f8028-b262-4e14-ab55-02ff22cc19ca","year":2000},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:29.027598Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:3fecaa9d25464246f84f431fd6417be6be23986b26fd0e28d91f3c0b0d71116f","observation_id":"52fd7e09-db83-4e6c-8f84-776dd16eb3ed","resolution":{"observed_at":"2026-08-07T14:15:30.915666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:30.682578Z","title":null,"venue":null,"work_id":"a0a1a8ce-97cb-493e-965f-314871bcd1c6","year":2019},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:29.169403Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:fd56ff31fb7f75641acadaa97a57041c43a49b1c7b0b23f6035124ed4cba6059","observation_id":"365b56f0-40c5-4265-b8e6-82d9931ba2f9","resolution":{"observed_at":"2026-08-07T14:15:30.772487Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:30.527230Z","title":"Tackling the objective inconsistency problem in heterogeneous federated optimization.Advances in neural information processing systems, 33:7611–7623, 2020","venue":null,"work_id":"2b69c12a-094f-4b31-89e0-d077483b7d03","year":2020},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:29.295392Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:cee74e5d31c5e4dd3d7b2cfa2d1974daaf01048e9368ed905c4efb421560b6f1","observation_id":"a850b5e5-57ad-43d2-bcf5-9af45db1c423","resolution":{"observed_at":"2026-08-07T14:15:30.586420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:15:30.342353Z","title":"Synchronization transitions in a disordered josephson series array.Physical review letters, 76(3):404, 1996","venue":null,"work_id":"093b7e49-245f-4d9e-aa48-88a998585905","year":1996},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:29.412650Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:705888c0fdb907e7c07fbc511f58998dc363454c3a8065a93ccc819998e94540","observation_id":"8043a969-1022-41f8-9230-4facc1696c86","resolution":{"observed_at":"2026-08-07T14:15:30.450597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-07-06T05:56:41.814255Z","submitted_at":"2017-08-25T14:01:29Z","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07747","snapshot_observed_at":"2026-08-07T14:15:29.530899Z","title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms.arXiv preprint arXiv:1708.07747, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:29.530899Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:ec2d43662687436743e78996c376ba85d2e8e15ab49a5a59ed31642fcfa13d4e","observation_id":"284801e1-6d62-4a6e-895a-264d5ef0a64c","resolution":{"observed_at":"2026-08-07T14:15:29.530899Z","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":"2026-08-07T14:15:29.670825Z","title":"Federated machine learning: Concept and applications.ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:29.670825Z"},"links":{"citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:a2e974d0b2e64586544c5eb08d58361d2ef1ac085ff8e2b3fff7e53bf3a668d3","observation_id":"06067c36-3a4b-49d2-8578-a16d283c3283","resolution":{"observed_at":"2026-08-07T14:15:29.670825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-07T14:15:29.794243Z","title":"Federated learning with non-iid data.arXiv preprint arXiv:1806.00582, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:29.794243Z"},"links":{"cited_paper":"/paper/1806.00582","citing_paper":"/paper/2505.19605"},"observation_digest":"sha256:88c9f553014640b8d9b33090fb4dfc628348224f4fd84553d2211be8393431aa","observation_id":"1204dcd6-3c86-4154-acd2-b78af41f33e4","resolution":{"observed_at":"2026-08-07T14:15:29.794243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.19605","last_updated":"2025-05-26T07:16:00Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T14:08:15.063944Z","submitted_at":"2025-05-26T07:16:00Z","title":"Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":1,"verified_fuzzy":16},"total_outbound_references":28},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2505.19605."}