{"as_of":"2026-08-08T02:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cfcacfe23a28c9ebd3ec2d8155fceacb2ec45aee86f6c099bb2704134198adab","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:52:36.516908Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-30T16:44:56.423356Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1906.06629","last_updated":"2019-10-09T22:53:13Z","snapshot_observed_at":"2026-07-06T08:00:37.173898Z","submitted_at":"2019-06-16T00:14:53Z","title":"Robust Federated Learning in a Heterogeneous Environment","version":2},"cited_work":{"arxiv_id":"1906.06629","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1906.06629","snapshot_observed_at":"2026-06-30T16:44:56.423356Z","title":"Robust fed- erated learning in a heterogeneous environment","venue":null,"work_id":"73f503d0-d326-4309-9ad9-088570b9293a","year":1906},"citing_paper":{"arxiv_id":"2407.09658","last_updated":"2026-04-09T17:42:49Z","snapshot_observed_at":"2026-07-06T18:45:39.741597Z","submitted_at":"2024-07-12T19:38:42Z","title":"BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-23T23:09:32.656257Z"},"links":{"cited_paper":"/paper/1906.06629","citing_paper":"/paper/2407.09658"},"observation_digest":"sha256:56772d11fb3b24406f48f3d4d0874e96051364cbe4d854af2e365210fa6d6ab5","observation_id":"32f55dc2-effc-4bc4-8f89-445189772e49","resolution":{"observed_at":"2026-05-23T23:13:37.009990Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06629","last_updated":"2019-10-09T22:53:13Z","snapshot_observed_at":"2026-07-06T08:00:37.173898Z","submitted_at":"2019-06-16T00:14:53Z","title":"Robust Federated Learning in a Heterogeneous Environment","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.06629","snapshot_observed_at":"2026-08-07T00:52:36.516908Z","title":"Robust federated learning in a heterogeneous environment,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.12754","last_updated":"2025-06-23T05:27:00Z","snapshot_observed_at":"2026-08-07T00:40:54.268456Z","submitted_at":"2025-06-15T07:42:46Z","title":"AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T00:52:36.516908Z"},"links":{"cited_paper":"/paper/1906.06629","citing_paper":"/paper/2506.12754"},"observation_digest":"sha256:f3aef9df4be9fae163e536402df5f0a7996ee37c8302be8154f6ae9ef27817fe","observation_id":"0a81d082-fa95-479c-97b2-b11bffe8b7d8","resolution":{"observed_at":"2026-08-07T00:52:36.516908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06629","last_updated":"2019-10-09T22:53:13Z","snapshot_observed_at":"2026-07-06T08:00:37.173898Z","submitted_at":"2019-06-16T00:14:53Z","title":"Robust Federated Learning in a Heterogeneous Environment","version":2},"cited_work":{"arxiv_id":"1906.06629","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1906.06629","snapshot_observed_at":"2026-06-30T16:44:56.423356Z","title":"Robust fed- erated learning in a heterogeneous environment","venue":null,"work_id":"73f503d0-d326-4309-9ad9-088570b9293a","year":1906},"citing_paper":{"arxiv_id":"2601.01901","last_updated":"2026-06-11T08:05:19Z","snapshot_observed_at":"2026-08-03T12:44:20.538831Z","submitted_at":"2026-01-05T08:46:11Z","title":"FedBiCross: Personalized One-Shot Federated Learning on Medical Images","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-21T16:45:46.966815Z"},"links":{"cited_paper":"/paper/1906.06629","citing_paper":"/paper/2601.01901"},"observation_digest":"sha256:9663d4c7b948829c6b9f9dc08c9744d612e5c1b39b7d8f2c760c07050af32798","observation_id":"0b92fcbd-a8d7-460c-8dfc-50284497832e","resolution":{"observed_at":"2026-05-21T16:50:23.812378Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06629","last_updated":"2019-10-09T22:53:13Z","snapshot_observed_at":"2026-07-06T08:00:37.173898Z","submitted_at":"2019-06-16T00:14:53Z","title":"Robust Federated Learning in a Heterogeneous Environment","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.06629","snapshot_observed_at":"2026-08-03T12:44:22.919563Z","title":"Robust federated learning in a heterogeneous environment,","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2601.01901","last_updated":"2026-06-11T08:05:19Z","snapshot_observed_at":"2026-08-03T12:44:20.538831Z","submitted_at":"2026-01-05T08:46:11Z","title":"FedBiCross: Personalized One-Shot Federated Learning on Medical Images","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T12:44:22.919563Z"},"links":{"cited_paper":"/paper/1906.06629","citing_paper":"/paper/2601.01901"},"observation_digest":"sha256:a195f851eb90e11393b79c909aa05c0397213ae1dbba1a6b7393f04a231affb3","observation_id":"59e46d42-ec17-462a-8bc6-1137d282e869","resolution":{"observed_at":"2026-08-03T12:44:22.919563Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06629","last_updated":"2019-10-09T22:53:13Z","snapshot_observed_at":"2026-07-06T08:00:37.173898Z","submitted_at":"2019-06-16T00:14:53Z","title":"Robust Federated Learning in a Heterogeneous Environment","version":2},"cited_work":{"arxiv_id":"1906.06629","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1906.06629","snapshot_observed_at":"2026-06-30T16:44:56.423356Z","title":"Robust fed- erated learning in a heterogeneous environment","venue":null,"work_id":"73f503d0-d326-4309-9ad9-088570b9293a","year":1906},"citing_paper":{"arxiv_id":"2604.06282","last_updated":"2026-04-07T11:45:55Z","snapshot_observed_at":"2026-08-03T00:20:10.688189Z","submitted_at":"2026-04-07T11:45:55Z","title":"Tight Convergence Rates for Online Distributed Linear Estimation with Adversarial Measurements","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T19:04:47.534620Z"},"links":{"cited_paper":"/paper/1906.06629","citing_paper":"/paper/2604.06282"},"observation_digest":"sha256:69095ef2cc5162cb831f22c56853c5ef4790082b796722b3f44ca518c23d7b93","observation_id":"890104e5-97fc-4581-ade3-99335ffe31c3","resolution":{"observed_at":"2026-05-10T23:30:51.034936Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06629","last_updated":"2019-10-09T22:53:13Z","snapshot_observed_at":"2026-07-06T08:00:37.173898Z","submitted_at":"2019-06-16T00:14:53Z","title":"Robust Federated Learning in a Heterogeneous Environment","version":2},"cited_work":{"arxiv_id":"1906.06629","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1906.06629","snapshot_observed_at":"2026-06-30T16:44:56.423356Z","title":"Robust fed- erated learning in a heterogeneous environment","venue":null,"work_id":"73f503d0-d326-4309-9ad9-088570b9293a","year":1906},"citing_paper":{"arxiv_id":"2605.09337","last_updated":"2026-05-10T05:24:02Z","snapshot_observed_at":"2026-08-02T15:50:32.604234Z","submitted_at":"2026-05-10T05:24:02Z","title":"Adversary-Robust Learning from Fully Asynchronous Directional Derivative Estimates","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-12T04:27:35.605021Z"},"links":{"cited_paper":"/paper/1906.06629","citing_paper":"/paper/2605.09337"},"observation_digest":"sha256:fcdaae366656316a5a2f0a2ac500697e27e3cbea9fc42a0375dd1ae70d94eead","observation_id":"a0e8b844-c973-4fe0-9caf-ec300837e8d6","resolution":{"observed_at":"2026-05-12T06:16:25.901731Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06629","last_updated":"2019-10-09T22:53:13Z","snapshot_observed_at":"2026-07-06T08:00:37.173898Z","submitted_at":"2019-06-16T00:14:53Z","title":"Robust Federated Learning in a Heterogeneous Environment","version":2},"cited_work":{"arxiv_id":"1906.06629","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1906.06629","snapshot_observed_at":"2026-06-30T16:44:56.423356Z","title":"Robust fed- erated learning in a heterogeneous environment","venue":null,"work_id":"73f503d0-d326-4309-9ad9-088570b9293a","year":1906},"citing_paper":{"arxiv_id":"2606.30615","last_updated":"2026-06-29T17:49:33Z","snapshot_observed_at":"2026-07-07T00:04:25.385438Z","submitted_at":"2026-06-29T17:49:33Z","title":"Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage","version":1},"reference_index":148,"source":"arxiv_source","source_observed_at":"2026-06-30T04:41:41.370083Z"},"links":{"cited_paper":"/paper/1906.06629","citing_paper":"/paper/2606.30615"},"observation_digest":"sha256:80357eb500c561afc14c7aa5573589281c88e2465cc0f01c9cc26bf4094d604e","observation_id":"ad3e286f-e1ac-451c-8ac6-29ea89a898d9","resolution":{"observed_at":"2026-06-30T16:44:56.424946Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06629","last_updated":"2019-10-09T22:53:13Z","snapshot_observed_at":"2026-07-06T08:00:37.173898Z","submitted_at":"2019-06-16T00:14:53Z","title":"Robust Federated Learning in a Heterogeneous Environment","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.06629","snapshot_observed_at":"2026-07-31T10:49:43.018688Z","title":"Robust federated learning in a heterogeneous environment","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2607.28338","last_updated":"2026-07-30T15:04:59Z","snapshot_observed_at":"2026-08-07T12:56:43.202970Z","submitted_at":"2026-07-30T15:04:59Z","title":"Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-31T10:49:43.018688Z"},"links":{"cited_paper":"/paper/1906.06629","citing_paper":"/paper/2607.28338"},"observation_digest":"sha256:eadc2078e54b0e465969c22cb400e8d94d24d1e5cc585c2c90dffcb4ab00d2b3","observation_id":"70fe5fcc-4d86-44bb-8617-b4b7b68555a5","resolution":{"observed_at":"2026-07-31T10:49:43.018688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1906.06629/citation-record","integrity":"/paper/1906.06629/integrity","json":"/paper/1906.06629/citation-record.json","paper":"/paper/1906.06629"},"outbound":[],"paper":{"arxiv_id":"1906.06629","last_updated":"2019-10-09T22:53:13Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T08:00:37.173898Z","submitted_at":"2019-06-16T00:14:53Z","title":"Robust Federated Learning in a Heterogeneous Environment"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1906.06629."}