{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:7SRSZ7BBLDXJDQLRUFK4EN3YDX","short_pith_number":"pith:7SRSZ7BB","canonical_record":{"source":{"id":"2310.04412","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-06T17:57:50Z","cross_cats_sorted":[],"title_canon_sha256":"25e4ec942f6eee9b79e09c74eed407dcd458162a509db6f263cf76322e239b26","abstract_canon_sha256":"13bda38e140c02004a561ac9900af64d12df6166f70606197cbb0aa0dd7b2df5"},"schema_version":"1.0"},"canonical_sha256":"fca32cfc2158ee91c171a155c237781de2ef69f3e8200d4c57d4c7ac6cef0974","source":{"kind":"arxiv","id":"2310.04412","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.04412","created_at":"2026-07-05T06:58:02Z"},{"alias_kind":"arxiv_version","alias_value":"2310.04412v1","created_at":"2026-07-05T06:58:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.04412","created_at":"2026-07-05T06:58:02Z"},{"alias_kind":"pith_short_12","alias_value":"7SRSZ7BBLDXJ","created_at":"2026-07-05T06:58:02Z"},{"alias_kind":"pith_short_16","alias_value":"7SRSZ7BBLDXJDQLR","created_at":"2026-07-05T06:58:02Z"},{"alias_kind":"pith_short_8","alias_value":"7SRSZ7BB","created_at":"2026-07-05T06:58:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:7SRSZ7BBLDXJDQLRUFK4EN3YDX","target":"record","payload":{"canonical_record":{"source":{"id":"2310.04412","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-06T17:57:50Z","cross_cats_sorted":[],"title_canon_sha256":"25e4ec942f6eee9b79e09c74eed407dcd458162a509db6f263cf76322e239b26","abstract_canon_sha256":"13bda38e140c02004a561ac9900af64d12df6166f70606197cbb0aa0dd7b2df5"},"schema_version":"1.0"},"canonical_sha256":"fca32cfc2158ee91c171a155c237781de2ef69f3e8200d4c57d4c7ac6cef0974","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:58:02.563303Z","signature_b64":"JJA3tLRdfrltYJUcTP4WsTZaEWgN6V70bJptU/FuQvyR1I2NSZlC7NvLCfOTXcJwSGOFKrOZ9wrX+Y9VbrTUBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fca32cfc2158ee91c171a155c237781de2ef69f3e8200d4c57d4c7ac6cef0974","last_reissued_at":"2026-07-05T06:58:02.562821Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:58:02.562821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.04412","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":"2026-07-05T06:58:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7W8jdAlNnK3rryGCf6pvEbEKuuhuUJD2NA0Xq/dahtBkJ2Oa+kOP+AHayFBW5AzGzMR5nrpOuZfpZSnixbBcAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:04:32.877712Z"},"content_sha256":"f93e1f7f9c80e2ac120f1f71e4f30e5fb0415f501e6a9fb67e60003fa92f2468","schema_version":"1.0","event_id":"sha256:f93e1f7f9c80e2ac120f1f71e4f30e5fb0415f501e6a9fb67e60003fa92f2468"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:7SRSZ7BBLDXJDQLRUFK4EN3YDX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alan Yuille, Cihang Xie, Jieru Mei, Liangqiong Qu, Peiran Xu, Yuyin Zhou, Zeyu Wang","submitted_at":"2023-10-06T17:57:50Z","abstract_excerpt":"Federated learning (FL) is an emerging paradigm in machine learning, where a shared model is collaboratively learned using data from multiple devices to mitigate the risk of data leakage. While recent studies posit that Vision Transformer (ViT) outperforms Convolutional Neural Networks (CNNs) in addressing data heterogeneity in FL, the specific architectural components that underpin this advantage have yet to be elucidated. In this paper, we systematically investigate the impact of different architectural elements, such as activation functions and normalization layers, on the performance withi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.04412","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/2310.04412/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":"2026-07-05T06:58:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WZdOesTfIQZKj1T8ltbHv2ONJFbumXk/uZTHypGJNCofeu8CKrj+uu3CLLrcraYwNBi5LBBeTULZJNiOfulWDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:04:32.878198Z"},"content_sha256":"6b39e51383887a4a0537542cb1455f9c59c6f5561f4360ae9b6a15f9a395ef50","schema_version":"1.0","event_id":"sha256:6b39e51383887a4a0537542cb1455f9c59c6f5561f4360ae9b6a15f9a395ef50"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7SRSZ7BBLDXJDQLRUFK4EN3YDX/bundle.json","state_url":"https://pith.science/pith/7SRSZ7BBLDXJDQLRUFK4EN3YDX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7SRSZ7BBLDXJDQLRUFK4EN3YDX/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":"2026-08-06T15:04:32Z","links":{"resolver":"https://pith.science/pith/7SRSZ7BBLDXJDQLRUFK4EN3YDX","bundle":"https://pith.science/pith/7SRSZ7BBLDXJDQLRUFK4EN3YDX/bundle.json","state":"https://pith.science/pith/7SRSZ7BBLDXJDQLRUFK4EN3YDX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7SRSZ7BBLDXJDQLRUFK4EN3YDX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:7SRSZ7BBLDXJDQLRUFK4EN3YDX","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":"13bda38e140c02004a561ac9900af64d12df6166f70606197cbb0aa0dd7b2df5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-06T17:57:50Z","title_canon_sha256":"25e4ec942f6eee9b79e09c74eed407dcd458162a509db6f263cf76322e239b26"},"schema_version":"1.0","source":{"id":"2310.04412","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.04412","created_at":"2026-07-05T06:58:02Z"},{"alias_kind":"arxiv_version","alias_value":"2310.04412v1","created_at":"2026-07-05T06:58:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.04412","created_at":"2026-07-05T06:58:02Z"},{"alias_kind":"pith_short_12","alias_value":"7SRSZ7BBLDXJ","created_at":"2026-07-05T06:58:02Z"},{"alias_kind":"pith_short_16","alias_value":"7SRSZ7BBLDXJDQLR","created_at":"2026-07-05T06:58:02Z"},{"alias_kind":"pith_short_8","alias_value":"7SRSZ7BB","created_at":"2026-07-05T06:58:02Z"}],"graph_snapshots":[{"event_id":"sha256:6b39e51383887a4a0537542cb1455f9c59c6f5561f4360ae9b6a15f9a395ef50","target":"graph","created_at":"2026-07-05T06:58:02Z","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/2310.04412/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning (FL) is an emerging paradigm in machine learning, where a shared model is collaboratively learned using data from multiple devices to mitigate the risk of data leakage. While recent studies posit that Vision Transformer (ViT) outperforms Convolutional Neural Networks (CNNs) in addressing data heterogeneity in FL, the specific architectural components that underpin this advantage have yet to be elucidated. In this paper, we systematically investigate the impact of different architectural elements, such as activation functions and normalization layers, on the performance withi","authors_text":"Alan Yuille, Cihang Xie, Jieru Mei, Liangqiong Qu, Peiran Xu, Yuyin Zhou, Zeyu Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-06T17:57:50Z","title":"FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.04412","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:f93e1f7f9c80e2ac120f1f71e4f30e5fb0415f501e6a9fb67e60003fa92f2468","target":"record","created_at":"2026-07-05T06:58:02Z","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":"13bda38e140c02004a561ac9900af64d12df6166f70606197cbb0aa0dd7b2df5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-06T17:57:50Z","title_canon_sha256":"25e4ec942f6eee9b79e09c74eed407dcd458162a509db6f263cf76322e239b26"},"schema_version":"1.0","source":{"id":"2310.04412","kind":"arxiv","version":1}},"canonical_sha256":"fca32cfc2158ee91c171a155c237781de2ef69f3e8200d4c57d4c7ac6cef0974","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fca32cfc2158ee91c171a155c237781de2ef69f3e8200d4c57d4c7ac6cef0974","first_computed_at":"2026-07-05T06:58:02.562821Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:58:02.562821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JJA3tLRdfrltYJUcTP4WsTZaEWgN6V70bJptU/FuQvyR1I2NSZlC7NvLCfOTXcJwSGOFKrOZ9wrX+Y9VbrTUBw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:58:02.563303Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.04412","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f93e1f7f9c80e2ac120f1f71e4f30e5fb0415f501e6a9fb67e60003fa92f2468","sha256:6b39e51383887a4a0537542cb1455f9c59c6f5561f4360ae9b6a15f9a395ef50"],"state_sha256":"bdf38f07ab825083fc970d510864cbcee146090cf68745614c804c5670a61407"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HuJ+Q+ErOOQjKF3hH+z7Bm1BH9QAzukFCQCxGLPPICdoByHmWN34gKHsfDRbqMCoIrH5PzOEvgVNQDtUpxXiCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T15:04:32.881430Z","bundle_sha256":"40c999ff0b522d41a357894ff44570b1f305f80c58f109da71a24277b8c54100"}}