{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UA5FW5U4HSWOYE4XRID5YFWGS4","short_pith_number":"pith:UA5FW5U4","schema_version":"1.0","canonical_sha256":"a03a5b769c3cacec13978a07dc16c697351d4e773195f46b1a4488b2424b54b0","source":{"kind":"arxiv","id":"2504.14188","version":2},"attestation_state":"computed","paper":{"title":"Rethinking Client-oriented Federated Graph Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Guoren Wang, Rong-Hua Li, Xunkai Li, Yinlin Zhu, Zekai Chen","submitted_at":"2025-04-19T05:24:40Z","abstract_excerpt":"As a new distributed graph learning paradigm, Federated Graph Learning (FGL) facilitates collaborative model training across local systems while preserving data privacy.\n  We review existing FGL approaches and categorize their optimization mechanisms into:\n  (1) Server-Client (S-C), where clients upload local model parameters for server-side aggregation and global updates;\n  (2) Client-Client (C-C), which allows direct exchange of information between clients and customizing their local training process.\n  We reveal that C-C shows superior potential due to its refined communication structure.\n "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.14188","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-19T05:24:40Z","cross_cats_sorted":[],"title_canon_sha256":"f320ae04c572e7c7e66ee6c921986f39e47e9f38a1f8b6894b0da2fc64e940ce","abstract_canon_sha256":"3cef2ae95423e1ecd500770b014f95b6fee58f8a15b0c0a6c685341a979e3480"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:45.764351Z","signature_b64":"NmcLJXMT76t/tSMvgu609i6BlCvIFPo0+z+g38r5pWwy1uRbPET5jabcqcS6ydmYDh9XH/8IjwHHVexKPqhXBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a03a5b769c3cacec13978a07dc16c697351d4e773195f46b1a4488b2424b54b0","last_reissued_at":"2026-07-05T11:53:45.763842Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:45.763842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rethinking Client-oriented Federated Graph Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Guoren Wang, Rong-Hua Li, Xunkai Li, Yinlin Zhu, Zekai Chen","submitted_at":"2025-04-19T05:24:40Z","abstract_excerpt":"As a new distributed graph learning paradigm, Federated Graph Learning (FGL) facilitates collaborative model training across local systems while preserving data privacy.\n  We review existing FGL approaches and categorize their optimization mechanisms into:\n  (1) Server-Client (S-C), where clients upload local model parameters for server-side aggregation and global updates;\n  (2) Client-Client (C-C), which allows direct exchange of information between clients and customizing their local training process.\n  We reveal that C-C shows superior potential due to its refined communication structure.\n "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14188","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.14188/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.14188","created_at":"2026-07-05T11:53:45.763891+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.14188v2","created_at":"2026-07-05T11:53:45.763891+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14188","created_at":"2026-07-05T11:53:45.763891+00:00"},{"alias_kind":"pith_short_12","alias_value":"UA5FW5U4HSWO","created_at":"2026-07-05T11:53:45.763891+00:00"},{"alias_kind":"pith_short_16","alias_value":"UA5FW5U4HSWOYE4X","created_at":"2026-07-05T11:53:45.763891+00:00"},{"alias_kind":"pith_short_8","alias_value":"UA5FW5U4","created_at":"2026-07-05T11:53:45.763891+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UA5FW5U4HSWOYE4XRID5YFWGS4","json":"https://pith.science/pith/UA5FW5U4HSWOYE4XRID5YFWGS4.json","graph_json":"https://pith.science/api/pith-number/UA5FW5U4HSWOYE4XRID5YFWGS4/graph.json","events_json":"https://pith.science/api/pith-number/UA5FW5U4HSWOYE4XRID5YFWGS4/events.json","paper":"https://pith.science/paper/UA5FW5U4"},"agent_actions":{"view_html":"https://pith.science/pith/UA5FW5U4HSWOYE4XRID5YFWGS4","download_json":"https://pith.science/pith/UA5FW5U4HSWOYE4XRID5YFWGS4.json","view_paper":"https://pith.science/paper/UA5FW5U4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.14188&json=true","fetch_graph":"https://pith.science/api/pith-number/UA5FW5U4HSWOYE4XRID5YFWGS4/graph.json","fetch_events":"https://pith.science/api/pith-number/UA5FW5U4HSWOYE4XRID5YFWGS4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UA5FW5U4HSWOYE4XRID5YFWGS4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UA5FW5U4HSWOYE4XRID5YFWGS4/action/storage_attestation","attest_author":"https://pith.science/pith/UA5FW5U4HSWOYE4XRID5YFWGS4/action/author_attestation","sign_citation":"https://pith.science/pith/UA5FW5U4HSWOYE4XRID5YFWGS4/action/citation_signature","submit_replication":"https://pith.science/pith/UA5FW5U4HSWOYE4XRID5YFWGS4/action/replication_record"}},"created_at":"2026-07-05T11:53:45.763891+00:00","updated_at":"2026-07-05T11:53:45.763891+00:00"}