{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:RYOH435FBCIUKW3ESPW4WJKNKK","short_pith_number":"pith:RYOH435F","canonical_record":{"source":{"id":"2506.01780","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-02T15:23:53Z","cross_cats_sorted":[],"title_canon_sha256":"28eef0a853e8d360843696d85d6631c25eb68a91448f787d1124d226ba180b36","abstract_canon_sha256":"0738de3da9f4b7a4a89086f76f04e380638f34fbd16440d5b905c1161b1a54fd"},"schema_version":"1.0"},"canonical_sha256":"8e1c7e6fa50891455b6493edcb254d529ca04970db8bc1b0f8a36d1b49273170","source":{"kind":"arxiv","id":"2506.01780","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.01780","created_at":"2026-07-05T11:14:19Z"},{"alias_kind":"arxiv_version","alias_value":"2506.01780v1","created_at":"2026-07-05T11:14:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01780","created_at":"2026-07-05T11:14:19Z"},{"alias_kind":"pith_short_12","alias_value":"RYOH435FBCIU","created_at":"2026-07-05T11:14:19Z"},{"alias_kind":"pith_short_16","alias_value":"RYOH435FBCIUKW3E","created_at":"2026-07-05T11:14:19Z"},{"alias_kind":"pith_short_8","alias_value":"RYOH435F","created_at":"2026-07-05T11:14:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:RYOH435FBCIUKW3ESPW4WJKNKK","target":"record","payload":{"canonical_record":{"source":{"id":"2506.01780","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-02T15:23:53Z","cross_cats_sorted":[],"title_canon_sha256":"28eef0a853e8d360843696d85d6631c25eb68a91448f787d1124d226ba180b36","abstract_canon_sha256":"0738de3da9f4b7a4a89086f76f04e380638f34fbd16440d5b905c1161b1a54fd"},"schema_version":"1.0"},"canonical_sha256":"8e1c7e6fa50891455b6493edcb254d529ca04970db8bc1b0f8a36d1b49273170","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:19.278854Z","signature_b64":"Axr1UR+Gw4pNOmPpJBZx2t4ZH40ca8FqbWfJy8ErcblOXpCZNHhL79y8sSQIg9jvXJ0639ikSi7F26GaZcanAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e1c7e6fa50891455b6493edcb254d529ca04970db8bc1b0f8a36d1b49273170","last_reissued_at":"2026-07-05T11:14:19.278359Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:19.278359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.01780","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":"2026-07-05T11:14:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wJedjSfcMC+JxkUY73WXPhbMkH+9pTGNOidzq8MRPeY0tKep2RRlQlaq4HrCEMDUJo40YnK+2J6Kl/UpSUWVBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:01:41.220024Z"},"content_sha256":"858a852229329ab9494601b728c3e8a63e5abdf99a7b6fb6d9fb2d5b484308a9","schema_version":"1.0","event_id":"sha256:858a852229329ab9494601b728c3e8a63e5abdf99a7b6fb6d9fb2d5b484308a9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:RYOH435FBCIUKW3ESPW4WJKNKK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Federated Gaussian Mixture Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Juan Carlos Andresen, Kuo-Yun Liang, Sophia Zhang Pettersson","submitted_at":"2025-06-02T15:23:53Z","abstract_excerpt":"This paper introduces FedGenGMM, a novel one-shot federated learning approach for Gaussian Mixture Models (GMM) tailored for unsupervised learning scenarios. In federated learning (FL), where multiple decentralized clients collaboratively train models without sharing raw data, significant challenges include statistical heterogeneity, high communication costs, and privacy concerns. FedGenGMM addresses these issues by allowing local GMM models, trained independently on client devices, to be aggregated through a single communication round. This approach leverages the generative property of GMMs, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01780","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/2506.01780/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":"2026-07-05T11:14:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/wCRZircrhMioubQ51mLaMQ6aWtgmKVVhX8MdVsgS0n0l+3Gcuek6zhkjGYRG8XFBawJW7JtKakZBvqU/vlXBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:01:41.220547Z"},"content_sha256":"938f43375937ad12b48c82fe4fa1daf75f37047cc31572dfeb012c588b094c3a","schema_version":"1.0","event_id":"sha256:938f43375937ad12b48c82fe4fa1daf75f37047cc31572dfeb012c588b094c3a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RYOH435FBCIUKW3ESPW4WJKNKK/bundle.json","state_url":"https://pith.science/pith/RYOH435FBCIUKW3ESPW4WJKNKK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RYOH435FBCIUKW3ESPW4WJKNKK/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":"2026-08-09T21:01:41Z","links":{"resolver":"https://pith.science/pith/RYOH435FBCIUKW3ESPW4WJKNKK","bundle":"https://pith.science/pith/RYOH435FBCIUKW3ESPW4WJKNKK/bundle.json","state":"https://pith.science/pith/RYOH435FBCIUKW3ESPW4WJKNKK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RYOH435FBCIUKW3ESPW4WJKNKK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:RYOH435FBCIUKW3ESPW4WJKNKK","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":"0738de3da9f4b7a4a89086f76f04e380638f34fbd16440d5b905c1161b1a54fd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-02T15:23:53Z","title_canon_sha256":"28eef0a853e8d360843696d85d6631c25eb68a91448f787d1124d226ba180b36"},"schema_version":"1.0","source":{"id":"2506.01780","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.01780","created_at":"2026-07-05T11:14:19Z"},{"alias_kind":"arxiv_version","alias_value":"2506.01780v1","created_at":"2026-07-05T11:14:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01780","created_at":"2026-07-05T11:14:19Z"},{"alias_kind":"pith_short_12","alias_value":"RYOH435FBCIU","created_at":"2026-07-05T11:14:19Z"},{"alias_kind":"pith_short_16","alias_value":"RYOH435FBCIUKW3E","created_at":"2026-07-05T11:14:19Z"},{"alias_kind":"pith_short_8","alias_value":"RYOH435F","created_at":"2026-07-05T11:14:19Z"}],"graph_snapshots":[{"event_id":"sha256:938f43375937ad12b48c82fe4fa1daf75f37047cc31572dfeb012c588b094c3a","target":"graph","created_at":"2026-07-05T11:14:19Z","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/2506.01780/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper introduces FedGenGMM, a novel one-shot federated learning approach for Gaussian Mixture Models (GMM) tailored for unsupervised learning scenarios. In federated learning (FL), where multiple decentralized clients collaboratively train models without sharing raw data, significant challenges include statistical heterogeneity, high communication costs, and privacy concerns. FedGenGMM addresses these issues by allowing local GMM models, trained independently on client devices, to be aggregated through a single communication round. This approach leverages the generative property of GMMs, ","authors_text":"Juan Carlos Andresen, Kuo-Yun Liang, Sophia Zhang Pettersson","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-02T15:23:53Z","title":"Federated Gaussian Mixture Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01780","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":"sha256:858a852229329ab9494601b728c3e8a63e5abdf99a7b6fb6d9fb2d5b484308a9","target":"record","created_at":"2026-07-05T11:14:19Z","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":"0738de3da9f4b7a4a89086f76f04e380638f34fbd16440d5b905c1161b1a54fd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-02T15:23:53Z","title_canon_sha256":"28eef0a853e8d360843696d85d6631c25eb68a91448f787d1124d226ba180b36"},"schema_version":"1.0","source":{"id":"2506.01780","kind":"arxiv","version":1}},"canonical_sha256":"8e1c7e6fa50891455b6493edcb254d529ca04970db8bc1b0f8a36d1b49273170","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8e1c7e6fa50891455b6493edcb254d529ca04970db8bc1b0f8a36d1b49273170","first_computed_at":"2026-07-05T11:14:19.278359Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:14:19.278359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Axr1UR+Gw4pNOmPpJBZx2t4ZH40ca8FqbWfJy8ErcblOXpCZNHhL79y8sSQIg9jvXJ0639ikSi7F26GaZcanAw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:14:19.278854Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.01780","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:858a852229329ab9494601b728c3e8a63e5abdf99a7b6fb6d9fb2d5b484308a9","sha256:938f43375937ad12b48c82fe4fa1daf75f37047cc31572dfeb012c588b094c3a"],"state_sha256":"26873190536f0ec6b2bf9c441b59c9f3e139689a8333dbc3573f0bce1cea0c59"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kGa27yAdWzI8pQp/4UvODkXP0TaPNXw9BcAm0JsJzOIiLAoVVnWy5kaZ3DU3KSvvgl3KJvyjPlityJDORO7nCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T21:01:41.224489Z","bundle_sha256":"06571aaed1acd1f8b2dfe8d690fe88e8f9cf6144d889a281b0e0e538f23b3faf"}}