{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:4GVHYVDKW4AL7JRTAUQZWKIM6K","short_pith_number":"pith:4GVHYVDK","canonical_record":{"source":{"id":"2401.08977","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-17T05:04:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8ad0314b08f40c1895fc09d382d7fc4474e14bc2a7aa7db197678925932899f7","abstract_canon_sha256":"6276282ed31282778884bcb6556ca55978415b8331e6e1f34b6a4f2b4eb66249"},"schema_version":"1.0"},"canonical_sha256":"e1aa7c546ab700bfa63305219b290cf29f9172942e84fd53a17c905aa28b3371","source":{"kind":"arxiv","id":"2401.08977","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.08977","created_at":"2026-07-05T07:53:50Z"},{"alias_kind":"arxiv_version","alias_value":"2401.08977v2","created_at":"2026-07-05T07:53:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.08977","created_at":"2026-07-05T07:53:50Z"},{"alias_kind":"pith_short_12","alias_value":"4GVHYVDKW4AL","created_at":"2026-07-05T07:53:50Z"},{"alias_kind":"pith_short_16","alias_value":"4GVHYVDKW4AL7JRT","created_at":"2026-07-05T07:53:50Z"},{"alias_kind":"pith_short_8","alias_value":"4GVHYVDK","created_at":"2026-07-05T07:53:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:4GVHYVDKW4AL7JRTAUQZWKIM6K","target":"record","payload":{"canonical_record":{"source":{"id":"2401.08977","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-17T05:04:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8ad0314b08f40c1895fc09d382d7fc4474e14bc2a7aa7db197678925932899f7","abstract_canon_sha256":"6276282ed31282778884bcb6556ca55978415b8331e6e1f34b6a4f2b4eb66249"},"schema_version":"1.0"},"canonical_sha256":"e1aa7c546ab700bfa63305219b290cf29f9172942e84fd53a17c905aa28b3371","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:53:50.070991Z","signature_b64":"ROqThtUriztuQHwHtl2qQAGtZ//6m3gVzYPeXWBFjR6QUgWc6b5dNyma0xjlSopLGAma6Js4R1gJHYac6lxKCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1aa7c546ab700bfa63305219b290cf29f9172942e84fd53a17c905aa28b3371","last_reissued_at":"2026-07-05T07:53:50.070524Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:53:50.070524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.08977","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:53:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sgfo2DRB9G3SixsVweLDt7ibepU4Z05cKSsN5LkIotw3uRwAmzRdUAp/8SY13KGhOGPBMrNhMGyg25WaCglfAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T19:34:05.894926Z"},"content_sha256":"1203cff4d515fca5e6ac5de14976ccb2a9d18e08f3d86391471c8dd1d50a0ad0","schema_version":"1.0","event_id":"sha256:1203cff4d515fca5e6ac5de14976ccb2a9d18e08f3d86391471c8dd1d50a0ad0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:4GVHYVDKW4AL7JRTAUQZWKIM6K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Howard Hao Yang, Jian Wu, Joey Tianyi Zhou, Liyinglan Liu, Wanlu Liu, Yang Feng, Zihan Chen, Zikai Xiao, Zuozhu Liu","submitted_at":"2024-01-17T05:04:33Z","abstract_excerpt":"Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from decentralized local clients manifests a globally prevalent long-tailed distribution, has garnered considerable attention in recent times. In the context of Fed-LT, existing works have predominantly centered on addressing the data imbalance issue to enhance the efficacy of the generic global model while neglecting the performance at the local level. In contrast, conventional Personalized Federated Learning (pFL) techniques are primarily devised to optimize personalized local models under the presumption of a balance"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.08977","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/2401.08977/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:53:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0klNczQCeAq2Tzg8QQqsvc2EEIR+a0STlA+l8cAZORiDCJEL64zAsx9o4qNGJydzGFf9KOftA4GyjbUz7cimAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T19:34:05.895474Z"},"content_sha256":"3141ab760b9a36cccc567543934296951041579987c49af3311370a4a1da0784","schema_version":"1.0","event_id":"sha256:3141ab760b9a36cccc567543934296951041579987c49af3311370a4a1da0784"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4GVHYVDKW4AL7JRTAUQZWKIM6K/bundle.json","state_url":"https://pith.science/pith/4GVHYVDKW4AL7JRTAUQZWKIM6K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4GVHYVDKW4AL7JRTAUQZWKIM6K/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T19:34:05Z","links":{"resolver":"https://pith.science/pith/4GVHYVDKW4AL7JRTAUQZWKIM6K","bundle":"https://pith.science/pith/4GVHYVDKW4AL7JRTAUQZWKIM6K/bundle.json","state":"https://pith.science/pith/4GVHYVDKW4AL7JRTAUQZWKIM6K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4GVHYVDKW4AL7JRTAUQZWKIM6K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4GVHYVDKW4AL7JRTAUQZWKIM6K","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6276282ed31282778884bcb6556ca55978415b8331e6e1f34b6a4f2b4eb66249","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-17T05:04:33Z","title_canon_sha256":"8ad0314b08f40c1895fc09d382d7fc4474e14bc2a7aa7db197678925932899f7"},"schema_version":"1.0","source":{"id":"2401.08977","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.08977","created_at":"2026-07-05T07:53:50Z"},{"alias_kind":"arxiv_version","alias_value":"2401.08977v2","created_at":"2026-07-05T07:53:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.08977","created_at":"2026-07-05T07:53:50Z"},{"alias_kind":"pith_short_12","alias_value":"4GVHYVDKW4AL","created_at":"2026-07-05T07:53:50Z"},{"alias_kind":"pith_short_16","alias_value":"4GVHYVDKW4AL7JRT","created_at":"2026-07-05T07:53:50Z"},{"alias_kind":"pith_short_8","alias_value":"4GVHYVDK","created_at":"2026-07-05T07:53:50Z"}],"graph_snapshots":[{"event_id":"sha256:3141ab760b9a36cccc567543934296951041579987c49af3311370a4a1da0784","target":"graph","created_at":"2026-07-05T07:53:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2401.08977/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from decentralized local clients manifests a globally prevalent long-tailed distribution, has garnered considerable attention in recent times. In the context of Fed-LT, existing works have predominantly centered on addressing the data imbalance issue to enhance the efficacy of the generic global model while neglecting the performance at the local level. In contrast, conventional Personalized Federated Learning (pFL) techniques are primarily devised to optimize personalized local models under the presumption of a balance","authors_text":"Howard Hao Yang, Jian Wu, Joey Tianyi Zhou, Liyinglan Liu, Wanlu Liu, Yang Feng, Zihan Chen, Zikai Xiao, Zuozhu Liu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-17T05:04:33Z","title":"FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.08977","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1203cff4d515fca5e6ac5de14976ccb2a9d18e08f3d86391471c8dd1d50a0ad0","target":"record","created_at":"2026-07-05T07:53:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6276282ed31282778884bcb6556ca55978415b8331e6e1f34b6a4f2b4eb66249","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-17T05:04:33Z","title_canon_sha256":"8ad0314b08f40c1895fc09d382d7fc4474e14bc2a7aa7db197678925932899f7"},"schema_version":"1.0","source":{"id":"2401.08977","kind":"arxiv","version":2}},"canonical_sha256":"e1aa7c546ab700bfa63305219b290cf29f9172942e84fd53a17c905aa28b3371","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1aa7c546ab700bfa63305219b290cf29f9172942e84fd53a17c905aa28b3371","first_computed_at":"2026-07-05T07:53:50.070524Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:53:50.070524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ROqThtUriztuQHwHtl2qQAGtZ//6m3gVzYPeXWBFjR6QUgWc6b5dNyma0xjlSopLGAma6Js4R1gJHYac6lxKCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:53:50.070991Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.08977","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1203cff4d515fca5e6ac5de14976ccb2a9d18e08f3d86391471c8dd1d50a0ad0","sha256:3141ab760b9a36cccc567543934296951041579987c49af3311370a4a1da0784"],"state_sha256":"f561c74f633e4dd64c77a0287f97d420b20fda4b2c61e3b30f56e81797d7067a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y2+2S1pcCw4xrIHI6J4AxFBhGqACvzJuIL3GkwvyAfmP88uN7uiBsr7IPV42fZ75OgTtSvmZJVYdQ0vrjo7sDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T19:34:05.904687Z","bundle_sha256":"c56df995a7ead627f0eb8a138e0ad733bffaf7f2dda15f5b855a22423c820c10"}}