{"as_of":"2026-08-23T23:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c4a699f4e9878b339db74034602f63210154aa4df1608cd9419fc9b4113839eb","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T10:45:00.353458Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-16T10:45:00.500030Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2208.13141","last_updated":"2023-10-10T23:04:48Z","snapshot_observed_at":"2026-08-16T16:36:27.129345Z","submitted_at":"2022-08-28T05:17:03Z","title":"Federated Learning of Large Models at the Edge via Principal Sub-Model Training","version":3},"cited_work":{"arxiv_id":"2208.13141","doi":null,"metadata_source":"pith","pith_arxiv_id":"2208.13141","snapshot_observed_at":"2026-08-16T10:45:00.500030Z","title":"Federated Learning of Large Models at the Edge via Principal Sub-Model Training","venue":"cs.LG","work_id":"a4e31fbf-bc26-41e2-b257-401480885f24","year":2022},"citing_paper":{"arxiv_id":"2504.17520","last_updated":"2025-04-24T13:02:54Z","snapshot_observed_at":"2026-08-22T10:16:00.361191Z","submitted_at":"2025-04-24T13:02:54Z","title":"Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T10:45:00.353458Z"},"links":{"cited_paper":"/paper/2208.13141","citing_paper":"/paper/2504.17520"},"observation_digest":"sha256:2d27bdd0c0053dc4826c5bad83f80a34e833145647290a937a6cd48c80884bed","observation_id":"f37b6b9d-c8e2-4490-9f7b-8bd0611921d6","resolution":{"observed_at":"2026-08-16T10:45:00.506791Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2208.13141/citation-record","integrity":"/paper/2208.13141/integrity","json":"/paper/2208.13141/citation-record.json","paper":"/paper/2208.13141"},"outbound":[],"paper":{"arxiv_id":"2208.13141","last_updated":"2023-10-10T23:04:48Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T16:36:27.129345Z","submitted_at":"2022-08-28T05:17:03Z","title":"Federated Learning of Large Models at the Edge via Principal Sub-Model Training"},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2208.13141."}