{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:MYNNTNIKGAQHOZHN52QNKZXGFA","short_pith_number":"pith:MYNNTNIK","canonical_record":{"source":{"id":"2412.14226","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-18T16:31:34Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5a1ba7f5f860a340ce2810ad4c2cd7753117af9fab6e09be9177241ddc5cee2b","abstract_canon_sha256":"4b8164eae0b8366b50c84c2313c45459df79eb40e4a003e0529698d8c1ad26e3"},"schema_version":"1.0"},"canonical_sha256":"661ad9b50a30207764edeea0d566e6282fbff39386f17d2284a290334aceeca2","source":{"kind":"arxiv","id":"2412.14226","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.14226","created_at":"2026-07-05T09:55:15Z"},{"alias_kind":"arxiv_version","alias_value":"2412.14226v2","created_at":"2026-07-05T09:55:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14226","created_at":"2026-07-05T09:55:15Z"},{"alias_kind":"pith_short_12","alias_value":"MYNNTNIKGAQH","created_at":"2026-07-05T09:55:15Z"},{"alias_kind":"pith_short_16","alias_value":"MYNNTNIKGAQHOZHN","created_at":"2026-07-05T09:55:15Z"},{"alias_kind":"pith_short_8","alias_value":"MYNNTNIK","created_at":"2026-07-05T09:55:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:MYNNTNIKGAQHOZHN52QNKZXGFA","target":"record","payload":{"canonical_record":{"source":{"id":"2412.14226","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-18T16:31:34Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5a1ba7f5f860a340ce2810ad4c2cd7753117af9fab6e09be9177241ddc5cee2b","abstract_canon_sha256":"4b8164eae0b8366b50c84c2313c45459df79eb40e4a003e0529698d8c1ad26e3"},"schema_version":"1.0"},"canonical_sha256":"661ad9b50a30207764edeea0d566e6282fbff39386f17d2284a290334aceeca2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:15.204591Z","signature_b64":"ldVNzQopxaAt6DYZdADmckC3BiLhsbrstAbhXLHj0xBpxlsnMDzDb5J7v+ONV/7DeYP3yktv8Iex9VTcNk1YAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"661ad9b50a30207764edeea0d566e6282fbff39386f17d2284a290334aceeca2","last_reissued_at":"2026-07-05T09:55:15.204201Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:15.204201Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.14226","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":"2026-07-05T09:55:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1oac9pwoqC8Hr940y3PS4R+g0twDv9vc0VOUnIB4G78u1QFPNlIlFZ2jB+efeUwwTWXjZCLX60Cu3VnijK7ODQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T07:04:29.546910Z"},"content_sha256":"cbf16120d73c40b6b5216439a32b98bb9004233ac8b69370943e3ce860abad5e","schema_version":"1.0","event_id":"sha256:cbf16120d73c40b6b5216439a32b98bb9004233ac8b69370943e3ce860abad5e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:MYNNTNIKGAQHOZHN52QNKZXGFA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dezheng Kong, Jordan Slessor, Linglong Kong, Xiaofen Tang, Zheng En Than","submitted_at":"2024-12-18T16:31:34Z","abstract_excerpt":"Federated learning (FL) is a machine learning methodology that involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving way. Several FL methods are introduced to tackle communication inefficiencies but do not address how to sample participating clients in each round effectively and in a privacy-preserving manner. In this paper, we propose \\textit{FedSTaS}, a client and data-level sampling method inspired by \\textit{FedSTS} and \\textit{FedSampling}. In each federated learning round, \\textit{FedSTaS} stratifies clients based on their com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14226","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/2412.14226/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":"2026-07-05T09:55:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UjUM1ePhZmtyr0YdOtsKIyNnpBJ2M/VTYGKirtXmnATRBUZ8epBbFvbCzDpTxU1+B2BNtQE35BtLLBIYw1saBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T07:04:29.547937Z"},"content_sha256":"4ba970b6168fb2461bea3dd613a2258be4c64f80b10a2255e91cb556e9fc9fd4","schema_version":"1.0","event_id":"sha256:4ba970b6168fb2461bea3dd613a2258be4c64f80b10a2255e91cb556e9fc9fd4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MYNNTNIKGAQHOZHN52QNKZXGFA/bundle.json","state_url":"https://pith.science/pith/MYNNTNIKGAQHOZHN52QNKZXGFA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MYNNTNIKGAQHOZHN52QNKZXGFA/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":"2026-08-13T07:04:29Z","links":{"resolver":"https://pith.science/pith/MYNNTNIKGAQHOZHN52QNKZXGFA","bundle":"https://pith.science/pith/MYNNTNIKGAQHOZHN52QNKZXGFA/bundle.json","state":"https://pith.science/pith/MYNNTNIKGAQHOZHN52QNKZXGFA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MYNNTNIKGAQHOZHN52QNKZXGFA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MYNNTNIKGAQHOZHN52QNKZXGFA","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":"4b8164eae0b8366b50c84c2313c45459df79eb40e4a003e0529698d8c1ad26e3","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-18T16:31:34Z","title_canon_sha256":"5a1ba7f5f860a340ce2810ad4c2cd7753117af9fab6e09be9177241ddc5cee2b"},"schema_version":"1.0","source":{"id":"2412.14226","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.14226","created_at":"2026-07-05T09:55:15Z"},{"alias_kind":"arxiv_version","alias_value":"2412.14226v2","created_at":"2026-07-05T09:55:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14226","created_at":"2026-07-05T09:55:15Z"},{"alias_kind":"pith_short_12","alias_value":"MYNNTNIKGAQH","created_at":"2026-07-05T09:55:15Z"},{"alias_kind":"pith_short_16","alias_value":"MYNNTNIKGAQHOZHN","created_at":"2026-07-05T09:55:15Z"},{"alias_kind":"pith_short_8","alias_value":"MYNNTNIK","created_at":"2026-07-05T09:55:15Z"}],"graph_snapshots":[{"event_id":"sha256:4ba970b6168fb2461bea3dd613a2258be4c64f80b10a2255e91cb556e9fc9fd4","target":"graph","created_at":"2026-07-05T09:55:15Z","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/2412.14226/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning (FL) is a machine learning methodology that involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving way. Several FL methods are introduced to tackle communication inefficiencies but do not address how to sample participating clients in each round effectively and in a privacy-preserving manner. In this paper, we propose \\textit{FedSTaS}, a client and data-level sampling method inspired by \\textit{FedSTS} and \\textit{FedSampling}. In each federated learning round, \\textit{FedSTaS} stratifies clients based on their com","authors_text":"Dezheng Kong, Jordan Slessor, Linglong Kong, Xiaofen Tang, Zheng En Than","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-18T16:31:34Z","title":"FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14226","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:cbf16120d73c40b6b5216439a32b98bb9004233ac8b69370943e3ce860abad5e","target":"record","created_at":"2026-07-05T09:55:15Z","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":"4b8164eae0b8366b50c84c2313c45459df79eb40e4a003e0529698d8c1ad26e3","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-18T16:31:34Z","title_canon_sha256":"5a1ba7f5f860a340ce2810ad4c2cd7753117af9fab6e09be9177241ddc5cee2b"},"schema_version":"1.0","source":{"id":"2412.14226","kind":"arxiv","version":2}},"canonical_sha256":"661ad9b50a30207764edeea0d566e6282fbff39386f17d2284a290334aceeca2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"661ad9b50a30207764edeea0d566e6282fbff39386f17d2284a290334aceeca2","first_computed_at":"2026-07-05T09:55:15.204201Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:55:15.204201Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ldVNzQopxaAt6DYZdADmckC3BiLhsbrstAbhXLHj0xBpxlsnMDzDb5J7v+ONV/7DeYP3yktv8Iex9VTcNk1YAA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:55:15.204591Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.14226","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cbf16120d73c40b6b5216439a32b98bb9004233ac8b69370943e3ce860abad5e","sha256:4ba970b6168fb2461bea3dd613a2258be4c64f80b10a2255e91cb556e9fc9fd4"],"state_sha256":"1c9afd9480207eaa21f4ecd7fc6b106d12961a6d99fefeb6d0998bffcc586e71"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pafjAzf8fJy1nH3wpIiNT3YOZWeEY9Z2E1NndOt+9GVzsvJXSMmC73rwdRESga8KqYTYfIJvoMrlZivSdbJFDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T07:04:29.553666Z","bundle_sha256":"cc90b486c4f9754edfc8ba190a1ae6c209fbdf1f0ab04b41b6d24a0d5d58b345"}}