{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:7G73G3QC5E6L7GZIGRUDU4KM3J","short_pith_number":"pith:7G73G3QC","canonical_record":{"source":{"id":"2312.08648","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-14T04:07:49Z","cross_cats_sorted":[],"title_canon_sha256":"6e1e42767d6b59cc976e2f911efe037afb190644ec14656e3fc870567630064d","abstract_canon_sha256":"0558f64b5cd3c8131ef5eeb9105b51c9a02a74a2000e0f88e32c8145f5eb0a54"},"schema_version":"1.0"},"canonical_sha256":"f9bfb36e02e93cbf9b2834683a714cda6308a5b14e0233a738ace7a7e1148e0c","source":{"kind":"arxiv","id":"2312.08648","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.08648","created_at":"2026-07-05T07:24:07Z"},{"alias_kind":"arxiv_version","alias_value":"2312.08648v1","created_at":"2026-07-05T07:24:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.08648","created_at":"2026-07-05T07:24:07Z"},{"alias_kind":"pith_short_12","alias_value":"7G73G3QC5E6L","created_at":"2026-07-05T07:24:07Z"},{"alias_kind":"pith_short_16","alias_value":"7G73G3QC5E6L7GZI","created_at":"2026-07-05T07:24:07Z"},{"alias_kind":"pith_short_8","alias_value":"7G73G3QC","created_at":"2026-07-05T07:24:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:7G73G3QC5E6L7GZIGRUDU4KM3J","target":"record","payload":{"canonical_record":{"source":{"id":"2312.08648","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-14T04:07:49Z","cross_cats_sorted":[],"title_canon_sha256":"6e1e42767d6b59cc976e2f911efe037afb190644ec14656e3fc870567630064d","abstract_canon_sha256":"0558f64b5cd3c8131ef5eeb9105b51c9a02a74a2000e0f88e32c8145f5eb0a54"},"schema_version":"1.0"},"canonical_sha256":"f9bfb36e02e93cbf9b2834683a714cda6308a5b14e0233a738ace7a7e1148e0c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:24:07.517420Z","signature_b64":"ZZW0C1W2JPauq27P+2ueD2DwXH/SXU6YNzWxYEl4UJ/vXgzjyAt6y9FQe5O/FH6MzR6kXOV/M2lpQGNRE2w6Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9bfb36e02e93cbf9b2834683a714cda6308a5b14e0233a738ace7a7e1148e0c","last_reissued_at":"2026-07-05T07:24:07.517022Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:24:07.517022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.08648","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:24:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xiKGgQZzWIVxdpM4a0b8oAiUE1cUvF1cFKZGzh2VzYcwxng6xZ7CSnC/cF31G329fiFi/TNQ7rJYzcbTv9cRCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T15:00:54.318561Z"},"content_sha256":"712802493639a421599aff58b76d2b2f4860b99b264942020680a145bae81b16","schema_version":"1.0","event_id":"sha256:712802493639a421599aff58b76d2b2f4860b99b264942020680a145bae81b16"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:7G73G3QC5E6L7GZIGRUDU4KM3J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiangming Shi, Shanshan Zheng, Xiangbo Yin, Yang Lu, Yanyun Qu, Yuan Xie","submitted_at":"2023-12-14T04:07:49Z","abstract_excerpt":"Federated learning (FL) provides a decentralized machine learning paradigm where a server collaborates with a group of clients to learn a global model without accessing the clients' data. User heterogeneity is a significant challenge for FL, which together with the class-distribution imbalance further enhances the difficulty of FL. Great progress has been made in large vision-language models, such as Contrastive Language-Image Pre-training (CLIP), which paves a new way for image classification and object recognition. Inspired by the success of CLIP on few-shot and zero-shot learning, we use CL"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.08648","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/2312.08648/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":"2026-07-05T07:24:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wQ345nPQu7Ao1UN7+G6z9EXbbJqAjUuJ0x8xKOIzn997MPT10V/VA57Gt/6MPvIreSSbaSU7Cv85c2HVztmhAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T15:00:54.319481Z"},"content_sha256":"b5432fa74f72ed8d8d1f2c0b96ce46886fe86ac697e77222e8a2b99977fe8377","schema_version":"1.0","event_id":"sha256:b5432fa74f72ed8d8d1f2c0b96ce46886fe86ac697e77222e8a2b99977fe8377"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7G73G3QC5E6L7GZIGRUDU4KM3J/bundle.json","state_url":"https://pith.science/pith/7G73G3QC5E6L7GZIGRUDU4KM3J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7G73G3QC5E6L7GZIGRUDU4KM3J/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":"2026-08-10T15:00:54Z","links":{"resolver":"https://pith.science/pith/7G73G3QC5E6L7GZIGRUDU4KM3J","bundle":"https://pith.science/pith/7G73G3QC5E6L7GZIGRUDU4KM3J/bundle.json","state":"https://pith.science/pith/7G73G3QC5E6L7GZIGRUDU4KM3J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7G73G3QC5E6L7GZIGRUDU4KM3J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:7G73G3QC5E6L7GZIGRUDU4KM3J","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":"0558f64b5cd3c8131ef5eeb9105b51c9a02a74a2000e0f88e32c8145f5eb0a54","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-14T04:07:49Z","title_canon_sha256":"6e1e42767d6b59cc976e2f911efe037afb190644ec14656e3fc870567630064d"},"schema_version":"1.0","source":{"id":"2312.08648","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.08648","created_at":"2026-07-05T07:24:07Z"},{"alias_kind":"arxiv_version","alias_value":"2312.08648v1","created_at":"2026-07-05T07:24:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.08648","created_at":"2026-07-05T07:24:07Z"},{"alias_kind":"pith_short_12","alias_value":"7G73G3QC5E6L","created_at":"2026-07-05T07:24:07Z"},{"alias_kind":"pith_short_16","alias_value":"7G73G3QC5E6L7GZI","created_at":"2026-07-05T07:24:07Z"},{"alias_kind":"pith_short_8","alias_value":"7G73G3QC","created_at":"2026-07-05T07:24:07Z"}],"graph_snapshots":[{"event_id":"sha256:b5432fa74f72ed8d8d1f2c0b96ce46886fe86ac697e77222e8a2b99977fe8377","target":"graph","created_at":"2026-07-05T07:24:07Z","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/2312.08648/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning (FL) provides a decentralized machine learning paradigm where a server collaborates with a group of clients to learn a global model without accessing the clients' data. User heterogeneity is a significant challenge for FL, which together with the class-distribution imbalance further enhances the difficulty of FL. Great progress has been made in large vision-language models, such as Contrastive Language-Image Pre-training (CLIP), which paves a new way for image classification and object recognition. Inspired by the success of CLIP on few-shot and zero-shot learning, we use CL","authors_text":"Jiangming Shi, Shanshan Zheng, Xiangbo Yin, Yang Lu, Yanyun Qu, Yuan Xie","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-14T04:07:49Z","title":"CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.08648","kind":"arxiv","version":1},"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":"sha256:712802493639a421599aff58b76d2b2f4860b99b264942020680a145bae81b16","target":"record","created_at":"2026-07-05T07:24:07Z","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":"0558f64b5cd3c8131ef5eeb9105b51c9a02a74a2000e0f88e32c8145f5eb0a54","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-14T04:07:49Z","title_canon_sha256":"6e1e42767d6b59cc976e2f911efe037afb190644ec14656e3fc870567630064d"},"schema_version":"1.0","source":{"id":"2312.08648","kind":"arxiv","version":1}},"canonical_sha256":"f9bfb36e02e93cbf9b2834683a714cda6308a5b14e0233a738ace7a7e1148e0c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f9bfb36e02e93cbf9b2834683a714cda6308a5b14e0233a738ace7a7e1148e0c","first_computed_at":"2026-07-05T07:24:07.517022Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:24:07.517022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZZW0C1W2JPauq27P+2ueD2DwXH/SXU6YNzWxYEl4UJ/vXgzjyAt6y9FQe5O/FH6MzR6kXOV/M2lpQGNRE2w6Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:24:07.517420Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.08648","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:712802493639a421599aff58b76d2b2f4860b99b264942020680a145bae81b16","sha256:b5432fa74f72ed8d8d1f2c0b96ce46886fe86ac697e77222e8a2b99977fe8377"],"state_sha256":"6b3cc577a79509a0452c210bc77e24efeb833b0f74b036a52a1f59e63a4a5134"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mGzenXnnM6fZQkXdCz6PR/bZ8C4yQQkBRDZxcS7SYvtkfhm1TgfEv++PxXsOps09e+AoC4ghBnkDESmgdt5QAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T15:00:54.326257Z","bundle_sha256":"2b352c389662c7cb370bf2968d4fce9c23050561496e33c1f2a9788451c96865"}}