{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:FL7P6TR56BESKX6Q5G7YHUDBEW","short_pith_number":"pith:FL7P6TR5","canonical_record":{"source":{"id":"2501.00379","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-31T10:24:15Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"347f41535891261afcfb88d62071e90680a64e73493d36bd2905d1e2d3674bd1","abstract_canon_sha256":"f809ff92b9f099df8b61a280f0e37bacc66848d2734f8b761abfeac6d6f81299"},"schema_version":"1.0"},"canonical_sha256":"2afeff4e3df049255fd0e9bf83d06125baefca7873c41bf47fc2301b4e404ba9","source":{"kind":"arxiv","id":"2501.00379","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.00379","created_at":"2026-07-05T09:55:44Z"},{"alias_kind":"arxiv_version","alias_value":"2501.00379v1","created_at":"2026-07-05T09:55:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00379","created_at":"2026-07-05T09:55:44Z"},{"alias_kind":"pith_short_12","alias_value":"FL7P6TR56BES","created_at":"2026-07-05T09:55:44Z"},{"alias_kind":"pith_short_16","alias_value":"FL7P6TR56BESKX6Q","created_at":"2026-07-05T09:55:44Z"},{"alias_kind":"pith_short_8","alias_value":"FL7P6TR5","created_at":"2026-07-05T09:55:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:FL7P6TR56BESKX6Q5G7YHUDBEW","target":"record","payload":{"canonical_record":{"source":{"id":"2501.00379","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-31T10:24:15Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"347f41535891261afcfb88d62071e90680a64e73493d36bd2905d1e2d3674bd1","abstract_canon_sha256":"f809ff92b9f099df8b61a280f0e37bacc66848d2734f8b761abfeac6d6f81299"},"schema_version":"1.0"},"canonical_sha256":"2afeff4e3df049255fd0e9bf83d06125baefca7873c41bf47fc2301b4e404ba9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:44.123534Z","signature_b64":"RM2OfDnxbkvbDYwz9mR5LsoX0ruMHiBcqSUxu+kw1jb0JnhK8t/ljO0II9uD3AdZ0/OM0LEj7Ev7qpPDr6FBBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2afeff4e3df049255fd0e9bf83d06125baefca7873c41bf47fc2301b4e404ba9","last_reissued_at":"2026-07-05T09:55:44.123031Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:44.123031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.00379","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":"2026-07-05T09:55:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rsf6hZuiP1X2jyOQUHGudpKZqy/7W06ibNdV8E8XMuzzrSa35GfknCuLdDAGbYZWAA1/NtA3t75KeCFgt6icBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:41:54.983381Z"},"content_sha256":"f40542274445ca1ad6433c6bc869e87292d73444902f1035c88906e54797405c","schema_version":"1.0","event_id":"sha256:f40542274445ca1ad6433c6bc869e87292d73444902f1035c88906e54797405c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:FL7P6TR56BESKX6Q5G7YHUDBEW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Federated Dropout: Convergence Analysis and Resource Allocation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Changsheng You, Dingzhu Wen, Kaibin Huang, Sijing Xie, Tharmalingam Ratnarajah, Xiaonan Liu","submitted_at":"2024-12-31T10:24:15Z","abstract_excerpt":"Federated Dropout is an efficient technique to overcome both communication and computation bottlenecks for deploying federated learning at the network edge. In each training round, an edge device only needs to update and transmit a sub-model, which is generated by the typical method of dropout in deep learning, and thus effectively reduces the per-round latency. \\textcolor{blue}{However, the theoretical convergence analysis for Federated Dropout is still lacking in the literature, particularly regarding the quantitative influence of dropout rate on convergence}. To address this issue, by using"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00379","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/2501.00379/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":"2026-07-05T09:55:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KMAtQkGqpwVAWirLtMCEjIJgNFuhaBzB8KRI+KBTFxIfdQE21cLeaJfT5vwzhVAeTT2LgkC56J1rS9b/IueJAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:41:54.983775Z"},"content_sha256":"c090af8dffc22c49b9c44855614fb01c5dcf7ed82941c7dd44a06a1ea1217eb7","schema_version":"1.0","event_id":"sha256:c090af8dffc22c49b9c44855614fb01c5dcf7ed82941c7dd44a06a1ea1217eb7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FL7P6TR56BESKX6Q5G7YHUDBEW/bundle.json","state_url":"https://pith.science/pith/FL7P6TR56BESKX6Q5G7YHUDBEW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FL7P6TR56BESKX6Q5G7YHUDBEW/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":"2026-08-03T17:41:54Z","links":{"resolver":"https://pith.science/pith/FL7P6TR56BESKX6Q5G7YHUDBEW","bundle":"https://pith.science/pith/FL7P6TR56BESKX6Q5G7YHUDBEW/bundle.json","state":"https://pith.science/pith/FL7P6TR56BESKX6Q5G7YHUDBEW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FL7P6TR56BESKX6Q5G7YHUDBEW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:FL7P6TR56BESKX6Q5G7YHUDBEW","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":"f809ff92b9f099df8b61a280f0e37bacc66848d2734f8b761abfeac6d6f81299","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-31T10:24:15Z","title_canon_sha256":"347f41535891261afcfb88d62071e90680a64e73493d36bd2905d1e2d3674bd1"},"schema_version":"1.0","source":{"id":"2501.00379","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.00379","created_at":"2026-07-05T09:55:44Z"},{"alias_kind":"arxiv_version","alias_value":"2501.00379v1","created_at":"2026-07-05T09:55:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00379","created_at":"2026-07-05T09:55:44Z"},{"alias_kind":"pith_short_12","alias_value":"FL7P6TR56BES","created_at":"2026-07-05T09:55:44Z"},{"alias_kind":"pith_short_16","alias_value":"FL7P6TR56BESKX6Q","created_at":"2026-07-05T09:55:44Z"},{"alias_kind":"pith_short_8","alias_value":"FL7P6TR5","created_at":"2026-07-05T09:55:44Z"}],"graph_snapshots":[{"event_id":"sha256:c090af8dffc22c49b9c44855614fb01c5dcf7ed82941c7dd44a06a1ea1217eb7","target":"graph","created_at":"2026-07-05T09:55:44Z","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/2501.00379/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated Dropout is an efficient technique to overcome both communication and computation bottlenecks for deploying federated learning at the network edge. In each training round, an edge device only needs to update and transmit a sub-model, which is generated by the typical method of dropout in deep learning, and thus effectively reduces the per-round latency. \\textcolor{blue}{However, the theoretical convergence analysis for Federated Dropout is still lacking in the literature, particularly regarding the quantitative influence of dropout rate on convergence}. To address this issue, by using","authors_text":"Changsheng You, Dingzhu Wen, Kaibin Huang, Sijing Xie, Tharmalingam Ratnarajah, Xiaonan Liu","cross_cats":["cs.IT","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-31T10:24:15Z","title":"Federated Dropout: Convergence Analysis and Resource Allocation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00379","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:f40542274445ca1ad6433c6bc869e87292d73444902f1035c88906e54797405c","target":"record","created_at":"2026-07-05T09:55:44Z","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":"f809ff92b9f099df8b61a280f0e37bacc66848d2734f8b761abfeac6d6f81299","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-31T10:24:15Z","title_canon_sha256":"347f41535891261afcfb88d62071e90680a64e73493d36bd2905d1e2d3674bd1"},"schema_version":"1.0","source":{"id":"2501.00379","kind":"arxiv","version":1}},"canonical_sha256":"2afeff4e3df049255fd0e9bf83d06125baefca7873c41bf47fc2301b4e404ba9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2afeff4e3df049255fd0e9bf83d06125baefca7873c41bf47fc2301b4e404ba9","first_computed_at":"2026-07-05T09:55:44.123031Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:55:44.123031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RM2OfDnxbkvbDYwz9mR5LsoX0ruMHiBcqSUxu+kw1jb0JnhK8t/ljO0II9uD3AdZ0/OM0LEj7Ev7qpPDr6FBBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:55:44.123534Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.00379","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f40542274445ca1ad6433c6bc869e87292d73444902f1035c88906e54797405c","sha256:c090af8dffc22c49b9c44855614fb01c5dcf7ed82941c7dd44a06a1ea1217eb7"],"state_sha256":"34e14ac39c49c9bd257e5ea83bf92c061f0655dd7f19a56534825f7dddb8e7b7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6/Cs6Kgv0ctGcCjQW/y+a7AsY8AS/OHJBVDaOhN0KN0mknIWzcEBe5i47q1soz4QCq0QbDJv25+1pX9pQNAtBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T17:41:54.986098Z","bundle_sha256":"c799c2bca7507f3aadd3fb804822ac51a35d376752d4b25ca6fdd7c8a97c2cd7"}}