{"as_of":"2026-08-21T01:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7828bea8715e20a679e293c64046988359c685e28f88d5c99d3d67020fe7d3b1","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T19:03:19.904535Z","state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2508.13625/citation-record","integrity":"/paper/2508.13625/integrity","json":"/paper/2508.13625/citation-record.json","paper":"/paper/2508.13625"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:03:20.388959Z","title":"Language models are few-shot learners,","venue":null,"work_id":"7ed043df-9918-4147-a65c-5576f2bf3fbd","year":2020},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.826086Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:a70cd010f058205ab168a116ce36b993bf8395595e5aaa71a283e756178763bb","observation_id":"9672b4b6-f93b-4428-bef0-47a27839893a","resolution":{"observed_at":"2026-08-05T19:03:20.392150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-15T12:33:55.451951Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-05T19:03:19.829855Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.829855Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:f80969f0dc39b7d06fee5f5032b461bf72063034b7fcb7ddded18fea6cf82381","observation_id":"225dccbb-4f41-482f-8732-4eaa6ea91ea0","resolution":{"observed_at":"2026-08-05T19:03:19.829855Z","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":"2026-08-05T19:03:20.379227Z","title":"Why do larger models generalize better? A theoretical perspective via the XOR problem,","venue":null,"work_id":"e04553da-de81-4a79-9e90-0e81cb27a02b","year":2019},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.833482Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:ee15486573c4eaf413cebb1f428e25eb71b376a38a007f82a081fb657d46ad7b","observation_id":"dc2a2468-f0d9-4f71-ba79-b151832eb187","resolution":{"observed_at":"2026-08-05T19:03:20.382993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.370617Z","title":null,"venue":null,"work_id":"5a8b3885-67bb-469d-ab2b-9bd4440dc32d","year":2022},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.837540Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:459940e2070935359153492ce3bdb7314dbc665ba61e63a6c27c384ec36183ed","observation_id":"59955804-643a-42ea-917a-4615943927a7","resolution":{"observed_at":"2026-08-05T19:03:20.373578Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.360930Z","title":"Advances and open problems in federated learning,","venue":null,"work_id":"96924e83-d7c8-4ec6-a824-cf410fcbf3ef","year":2021},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.841137Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:b3e98ac0dd8bc76ca1df0cf371b237c4460b934023da6c1b81048e34fe04afd4","observation_id":"2dfdc053-076f-4180-aced-07d0edb6a011","resolution":{"observed_at":"2026-08-05T19:03:20.364100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.351760Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":"cb81ff80-8af6-4af7-8237-f99d9ce9ae9d","year":2017},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.844991Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:5900c9c1946f52ce32ee02e82f4b09f889635069058e995f67dd28219371be30","observation_id":"d8e4fd4a-fe97-4d31-bba8-b32e8f016ac3","resolution":{"observed_at":"2026-08-05T19:03:20.354848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.341978Z","title":"Federated optimization in heterogeneous networks,","venue":null,"work_id":"4517f14a-e1d1-4b42-8548-a6278ff1e225","year":2020},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.848805Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:770652eb00992bc60e31cb9ea8216de651af604ef806fac29fc70f2f13d5c08f","observation_id":"04eea3a3-41dc-4a79-b488-20d0c19c2ef2","resolution":{"observed_at":"2026-08-05T19:03:20.345258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.224554Z","title":"FLaaS6G: Federated learning as a service in 6G using distributed data management architec- ture,","venue":null,"work_id":"ab233f5e-3e02-41cd-8e82-1aa1e16e8058","year":2022},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.851876Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:8b524ca32da0fa9d4a96a02092c3d129b4e5c75f10b6eadd316882f5e8f71b3a","observation_id":"fc34996f-03d9-4990-a1c3-058e56e33a27","resolution":{"observed_at":"2026-08-05T19:03:20.227966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.215264Z","title":"Advancing federated learning in 6G: A trusted architecture with graph-based analysis,","venue":null,"work_id":"a16d2817-aa6c-4890-8e65-17b97ae63e2a","year":2023},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.855051Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:865bfb66e01a06bc3608526664c126b572b88d8145879b542ac316a29e73ee7e","observation_id":"b84e3eea-ef9a-48a3-86c3-f5f507d49e14","resolution":{"observed_at":"2026-08-05T19:03:20.218542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-08-16T18:00:58.008096Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-05T19:03:19.858079Z","title":"Distilling the knowledge in a neural network,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.858079Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:dfd0e45c992d8bb70fae3cc50f96d57d9905617df12d8fde978ff8e40a32b740","observation_id":"d4c9a823-6692-476d-9c32-13beb5a04937","resolution":{"observed_at":"2026-08-05T19:03:19.858079Z","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":"2026-08-05T19:03:20.178219Z","title":"Het- erogeneous ensemble knowledge transfer for training large models in federated learning,","venue":null,"work_id":"4200e073-1656-408c-a11d-43b638bd9a81","year":2022},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.861734Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:0027177162918b70df7d24842499e0cde4437e8f052ad11af17a9e5ce1ed2652","observation_id":"51f196be-1700-4fb3-bb01-913540997caf","resolution":{"observed_at":"2026-08-05T19:03:20.182185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2110.11027","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:03:20.042160Z","title":"Fedgems: Federated learning of larger server models via selective knowledge fusion,","venue":null,"work_id":"35428b71-64b3-4643-b922-ff9993cea9c2","year":2021},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.865517Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:90cd78c2f2486d9d83feb661146ef34bf89e89736ac87691c0b900ee09bf3db1","observation_id":"ae8d8092-2cfc-4bf8-94ef-4bf63b23aa5d","resolution":{"observed_at":"2026-08-05T19:03:20.049049Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.167425Z","title":"Ensemble distillation for robust model fusion in federated learning,","venue":null,"work_id":"1c8825da-94c4-4b70-8ae2-3bcce5a54a7a","year":2020},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.868929Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:71d1d8994830593a43e2b7a8591d546786237ec70227131316d579e4fec7aae6","observation_id":"23048c17-7fd6-4df0-8ed6-12e61f9d47bd","resolution":{"observed_at":"2026-08-05T19:03:20.171558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.158819Z","title":"Not all knowledge is created equal: Mutual distillation of confident knowledge,","venue":null,"work_id":"6a153de4-d88d-4b23-af7c-8092e9444417","year":2022},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.872168Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:1a574856ebfe8564943625df328f9740bf6c371ddc103c101dd86599ad9d84e2","observation_id":"5c3c104f-0869-4745-a76f-cc28d91a9f11","resolution":{"observed_at":"2026-08-05T19:03:20.161807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.08119","last_updated":"2021-09-16T17:13:53Z","snapshot_observed_at":"2026-08-16T17:56:04.706078Z","submitted_at":"2021-09-16T17:13:53Z","title":"Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer","version":1},"cited_work":{"arxiv_id":"2109.08119","doi":null,"metadata_source":"pith","pith_arxiv_id":"2109.08119","snapshot_observed_at":"2026-08-05T19:03:19.944270Z","title":"Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer","venue":"cs.LG","work_id":"51c79f12-35ce-411f-b867-f870ca6ad4ea","year":2021},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.875122Z"},"links":{"cited_paper":"/paper/2109.08119","citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:aec2143756246e866f9807cca262021e831dd8bbf4c5a0ee9edaf405ffc72b30","observation_id":"44d6bf26-38a3-4b86-ad3b-c1c0c1d0c3d2","resolution":{"observed_at":"2026-08-05T19:03:19.949842Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.149471Z","title":"A hierarchical knowledge transfer framework for heterogeneous federated learning,","venue":null,"work_id":"5f131de6-553d-4b70-864b-c4e3cae9dd33","year":2023},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.878794Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:8c01357896bbb55b159ba87cbb45a0fedabd4eb886181b517054058c8e61d0ff","observation_id":"cfc9bfef-ced3-438a-b364-c641d8f5e56b","resolution":{"observed_at":"2026-08-05T19:03:20.152685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.11175","last_updated":"2019-03-05T20:33:39Z","snapshot_observed_at":"2026-08-17T05:34:24.192665Z","submitted_at":"2019-02-28T15:55:18Z","title":"One-Shot Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.11175","snapshot_observed_at":"2026-08-05T19:03:19.882018Z","title":"One-shot federated learning,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.882018Z"},"links":{"cited_paper":"/paper/1902.11175","citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:5bc82200fee62b730a558540b63642a80ec7e09ef715fd40aba10bf462f452bb","observation_id":"d58f7418-c4ba-403d-b315-fc7b99f090c6","resolution":{"observed_at":"2026-08-05T19:03:19.882018Z","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":"2026-08-05T19:03:20.140839Z","title":"Dense: Data-free one-shot federated learning,","venue":null,"work_id":"8ca2bfb7-2505-4b16-b1e3-7f0b44ded267","year":2022},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.885759Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:470199519572ddb82d08119703d40f13cca4e5bd7647b30aa16bf163e82c2425","observation_id":"8e2db379-3fb8-4141-9cb7-fda0566cac02","resolution":{"observed_at":"2026-08-05T19:03:20.143687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.131418Z","title":"Towards addressing label skews in one-shot federated learning,","venue":null,"work_id":"7e32bc82-b587-43f1-b171-a1f72c8a23e2","year":2023},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.888906Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:0863906c74ee09d1a734324cfff0ae416e395ead41d49fef02395e5daa121e39","observation_id":"aa004ce5-43a6-4af0-80a6-3598d35a5d7d","resolution":{"observed_at":"2026-08-05T19:03:20.134761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.121713Z","title":"Semi-supervised learning by entropy minimization,","venue":null,"work_id":"337f0171-b08c-4f16-9ea4-7e85f7de8a42","year":2004},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.892019Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:7bb44366958bb5d0e9432c564f1bf1a70267925e94663ea3c0ab2e52c9c8441a","observation_id":"536242f1-4276-42e4-a492-dd427749d616","resolution":{"observed_at":"2026-08-05T19:03:20.124786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.111228Z","title":"Self-paced curriculum learning,","venue":null,"work_id":"696a5e0a-dc4a-4348-9255-27b751676f7a","year":2015},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.895256Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:359c3d7d79a2a0139ab00eb3d0201160cb9b54e5b82fc8272d51248a73602094","observation_id":"32593e39-70d5-40ab-a73a-5a458eef73d0","resolution":{"observed_at":"2026-08-05T19:03:20.114600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.099997Z","title":"Nlnl: Negative learning for noisy labels,","venue":null,"work_id":"df9a58b5-9b2c-49b8-9a8e-adcf28a5b7f9","year":2019},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.898187Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:6ccb0df6345f9e6a67bf72b4ced0125c980952b7eba04c7c8955163698267e2a","observation_id":"8067f7a3-a238-4a8f-9d5f-e2000d556e21","resolution":{"observed_at":"2026-08-05T19:03:20.103590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.089671Z","title":"Federated learning on non-iid data silos: An experimental study,","venue":null,"work_id":"a92eea43-af2c-4e2c-82bd-358bd754ac88","year":2022},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.901446Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:790daeee702b9c4c239277e09c2de03888cc7c9fb4ada2ea24c2e04d3dba1cd1","observation_id":"308f3e33-3b55-4708-8e05-b95150749613","resolution":{"observed_at":"2026-08-05T19:03:20.092820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T19:03:20.079017Z","title":"Practical one-shot federated learning for cross-silo setting,","venue":null,"work_id":"72cdc008-cf42-4672-b8dc-62daaca59b1f","year":2021},"citing_paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:19.904535Z"},"links":{"citing_paper":"/paper/2508.13625"},"observation_digest":"sha256:e4aad81f31c811eb79e1b1d7b553abaf134128d008805c2b4e19429ae761f15b","observation_id":"b87d17cf-9659-4b3a-aafd-276cfdba3b29","resolution":{"observed_at":"2026-08-05T19:03:20.082412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.13625","last_updated":"2025-08-19T08:35:25Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T17:33:40.154102Z","submitted_at":"2025-08-19T08:35:25Z","title":"Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":2,"verified_fuzzy":18},"total_outbound_references":24},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2508.13625."}