{"as_of":"2026-08-14T22:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:44b83205266cdf618f894ef0cfb9d2eb4d153906086193e18b5bba429663e0dc","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":"2026-08-14T06:32:32.682623+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":"2026-08-10T07:49:09.846041Z","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":"sha256:790d9d3319e54ce84bda2700c211002baf5e2a90563a84396bdc4ab24b5541b5","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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":"sha256:811de88ace66d335b9e04218dd262b700c2f3eac68b461e45604b43a586a8b43","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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:c90348b9e4b97902281046145abf39cbd5ddc07d0503072587268eaa0ef7f3eb","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":"2026-08-10T07:49:09.846041Z","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":"sha256:73ec8581858b615d06d98ca63873a98ea1f4075139dd6ffc5401ddeb49b570a2","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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":"sha256:2a5de41ec4ccdc32c206475a37728ce6305cdf5b6acbe91b0c119054a9386749","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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:bc465990fe21e4668c288842c7972a1332f2ffeff2f95369ecb6c23a5e5a7c68","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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:afc64946c609a2e843948e709fbea2d00d9fdd20c9ca24047f71093cc83ad69f","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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:c7c1c496fe4920e5247cd061fbc59750bee003cf173d37dcd44d9b921a097d2b","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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":"sha256:43a5b72eed2049f53c9468b7ce3ddeba2ede8ff799055706966a7253389175b1","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":"2026-08-10T07:49:09.846041Z","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":"sha256:05ee0dcbfe81fd6c9fab526bc22296b95428e0146f4db7daf5bd1089451155e1","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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":"sha256:6cdfade0b794c615f25cd869e2e09c190bd5b654127b75c708c2d0d6e1d36dcc","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":"2026-08-09T15:59:31.104495Z","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":"2026-08-10T07:49:09.846041Z","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":"sha256:2642d33561bac8db5fa1de3182c802ee6696f31b1164284609ed8c7becad2efb","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":"2026-08-10T07:49:09.846041Z","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":"sha256:36d6992f906452949ff270cc7a3fd28c89be4d6d01b358e0e4ac55f1b103fa52","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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":"sha256:182c298960a2b724b4603324a792f0c0584116c6ad7e09de0c92d09fdbbd3b66","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":"2026-08-14T22:21:10.582660Z","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":"2026-08-10T07:49:09.846041Z","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":"sha256:3f3d6ee8523cedd5d690be4ce7acbf8662a57325c356d3c868f40181c7c301fc","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":"2026-08-10T07:49:09.846041Z","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:ccb0151148a70f0a5f47bbdf5b72ab8d6be903618af2614f56b597ef21be6b51","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00451","last_updated":"2018-06-01T17:16:56Z","snapshot_observed_at":"2026-08-14T19:08:27.314876Z","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":"2026-08-10T07:49:09.846041Z","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:e788fdc69126fb26840285527254504e8b4d943de103f692569d3e89d77024e1","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":"2026-08-10T07:49:09.846041Z","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":"sha256:867d6255bdadc512de8af6efeb098f3da4980ff36b65d3f77d2625bd444bc825","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00182","last_updated":"2025-06-03T16:38:42Z","snapshot_observed_at":"2026-08-09T19:51:15.675914Z","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":"2026-08-10T07:49:09.846041Z","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":"sha256:300e3140c5e2b2ef8b8ef6629637253f8d8eb01a28304aec0afb4f4f442f134a","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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:a4b2595e142a5d01e2e71909d1d14902c22c7f10b5b9fd039158737448c7cf78","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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":"sha256:2328962dc584eedcf266531ada2b08ebb23da300345789a2a1a6e3e51fd88333","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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":"sha256:96f17163977e33d84d9e52c0969e66e73e7288f4f37dd83f1b4143390efba7b8","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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":"sha256:58d9970ea8ad147fec51884f0b663a56f59a4812abf207399e00b1f1c0781395","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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:e706d44ca9db64ba07aaaf230b6a16c1b6f4fb4e63f9959a5701d773fc93f2cd","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T07:49:09.846041Z","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":"sha256:5d3cfe78bcc41cd7ec07c78c2c1964c2f8c8c1c509a438f50cd3756bdae6fb7e","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 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-08-13T15:13:33.081929Z","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":"2026-08-10T07:49:09.846041Z","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":"sha256:9889d17343ab30d43894cb5cafdf7d644b22afd24c7417f8adc7e0e791a0aeff","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":"2026-08-10T07:49:09.846041Z","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":"sha256:07929891a3caac39d37023620cfb97bf6933e844c6351e1f90b0aaa99648fb28","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":"2026-08-10T07:49:09.846041Z","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":"sha256:9578096bfdb4d7a55337b255090560bbc7690e472bf6f440c3ae30996bbd3a88","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":"2026-08-10T07:49:09.846041Z","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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2505.19605."}