{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:6UFHPKBF75AXUUB34BC5TKZUHT","short_pith_number":"pith:6UFHPKBF","schema_version":"1.0","canonical_sha256":"f50a77a825ff417a503be045d9ab343cdab5b706e4bf45b64fde23c509ad16ad","source":{"kind":"arxiv","id":"2607.08784","version":1},"attestation_state":"computed","paper":{"title":"HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DC"],"primary_cat":"cs.LG","authors_text":"Anh Tran Nam Nguyet, Dung D. Le, Kok-Seng Wong, Le-Tuan Nguyen, Minh-Duong Nguyen, Nhi Trinh, Thinh T. H. Nguyen","submitted_at":"2026-06-13T10:32:34Z","abstract_excerpt":"Federated continual learning (FCL) evaluates how distributed clients learn from changing data streams while retaining previously learned knowledge. Existing evaluations are difficult to compare because they often change datasets, task splits, client data splits, task orders, backbones, memory assumptions, and reporting rules simultaneously. We introduce \\textbf{HERO}, a heterogeneity-aware benchmark library for FCL. HERO builds benchmark streams by separating three choices that are often coupled, namely the task split, the client data split, and the client task sequence. In HERO-Core, the main"},"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":"2607.08784","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-13T10:32:34Z","cross_cats_sorted":["cs.AI","cs.DC"],"title_canon_sha256":"cecd34eee98f6955a99181f429deb29d9ac9ad69ca666edc08a3935495f56834","abstract_canon_sha256":"e14bbed0a78988fa9c03d4267d8ddb2a5b754d948e1933002e5c4d08f1348d86"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-13T00:17:16.474379Z","signature_b64":"YU6hSlX68WDQDLrdmZ6huFrd3Fnd0BqaSbxwDdk/2T1swPPkcf+6kb7krlW7TDBZ0DkHXz9DSOb9NGvqyih1DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f50a77a825ff417a503be045d9ab343cdab5b706e4bf45b64fde23c509ad16ad","last_reissued_at":"2026-07-13T00:17:16.473226Z","signature_status":"signed_v1","first_computed_at":"2026-07-13T00:17:16.473226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DC"],"primary_cat":"cs.LG","authors_text":"Anh Tran Nam Nguyet, Dung D. Le, Kok-Seng Wong, Le-Tuan Nguyen, Minh-Duong Nguyen, Nhi Trinh, Thinh T. H. Nguyen","submitted_at":"2026-06-13T10:32:34Z","abstract_excerpt":"Federated continual learning (FCL) evaluates how distributed clients learn from changing data streams while retaining previously learned knowledge. Existing evaluations are difficult to compare because they often change datasets, task splits, client data splits, task orders, backbones, memory assumptions, and reporting rules simultaneously. We introduce \\textbf{HERO}, a heterogeneity-aware benchmark library for FCL. HERO builds benchmark streams by separating three choices that are often coupled, namely the task split, the client data split, and the client task sequence. In HERO-Core, the main"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.08784","kind":"arxiv","version":1},"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/2607.08784/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":"2607.08784","created_at":"2026-07-13T00:17:16.473777+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.08784v1","created_at":"2026-07-13T00:17:16.473777+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.08784","created_at":"2026-07-13T00:17:16.473777+00:00"},{"alias_kind":"pith_short_12","alias_value":"6UFHPKBF75AX","created_at":"2026-07-13T00:17:16.473777+00:00"},{"alias_kind":"pith_short_16","alias_value":"6UFHPKBF75AXUUB3","created_at":"2026-07-13T00:17:16.473777+00:00"},{"alias_kind":"pith_short_8","alias_value":"6UFHPKBF","created_at":"2026-07-13T00:17:16.473777+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/6UFHPKBF75AXUUB34BC5TKZUHT","json":"https://pith.science/pith/6UFHPKBF75AXUUB34BC5TKZUHT.json","graph_json":"https://pith.science/api/pith-number/6UFHPKBF75AXUUB34BC5TKZUHT/graph.json","events_json":"https://pith.science/api/pith-number/6UFHPKBF75AXUUB34BC5TKZUHT/events.json","paper":"https://pith.science/paper/6UFHPKBF"},"agent_actions":{"view_html":"https://pith.science/pith/6UFHPKBF75AXUUB34BC5TKZUHT","download_json":"https://pith.science/pith/6UFHPKBF75AXUUB34BC5TKZUHT.json","view_paper":"https://pith.science/paper/6UFHPKBF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.08784&json=true","fetch_graph":"https://pith.science/api/pith-number/6UFHPKBF75AXUUB34BC5TKZUHT/graph.json","fetch_events":"https://pith.science/api/pith-number/6UFHPKBF75AXUUB34BC5TKZUHT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6UFHPKBF75AXUUB34BC5TKZUHT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6UFHPKBF75AXUUB34BC5TKZUHT/action/storage_attestation","attest_author":"https://pith.science/pith/6UFHPKBF75AXUUB34BC5TKZUHT/action/author_attestation","sign_citation":"https://pith.science/pith/6UFHPKBF75AXUUB34BC5TKZUHT/action/citation_signature","submit_replication":"https://pith.science/pith/6UFHPKBF75AXUUB34BC5TKZUHT/action/replication_record"}},"created_at":"2026-07-13T00:17:16.473777+00:00","updated_at":"2026-07-13T00:17:16.473777+00:00"}