{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:P5LGVVBBCHU3FCVVIKTBLMQV6K","short_pith_number":"pith:P5LGVVBB","canonical_record":{"source":{"id":"2302.04228","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-08T17:58:11Z","cross_cats_sorted":[],"title_canon_sha256":"ae7840e889b03e726ab5c9f64574710b58d6d1115ac56229b022c141542fb65a","abstract_canon_sha256":"f00a4e346831175ecbcee5ab131b3eda845b9e0f691f70e228dbf655d8b7137b"},"schema_version":"1.0"},"canonical_sha256":"7f566ad42111e9b28ab542a615b215f2a9e671e7f35bf653c3f8841614cff10f","source":{"kind":"arxiv","id":"2302.04228","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.04228","created_at":"2026-07-05T05:40:02Z"},{"alias_kind":"arxiv_version","alias_value":"2302.04228v1","created_at":"2026-07-05T05:40:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.04228","created_at":"2026-07-05T05:40:02Z"},{"alias_kind":"pith_short_12","alias_value":"P5LGVVBBCHU3","created_at":"2026-07-05T05:40:02Z"},{"alias_kind":"pith_short_16","alias_value":"P5LGVVBBCHU3FCVV","created_at":"2026-07-05T05:40:02Z"},{"alias_kind":"pith_short_8","alias_value":"P5LGVVBB","created_at":"2026-07-05T05:40:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:P5LGVVBBCHU3FCVVIKTBLMQV6K","target":"record","payload":{"canonical_record":{"source":{"id":"2302.04228","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-08T17:58:11Z","cross_cats_sorted":[],"title_canon_sha256":"ae7840e889b03e726ab5c9f64574710b58d6d1115ac56229b022c141542fb65a","abstract_canon_sha256":"f00a4e346831175ecbcee5ab131b3eda845b9e0f691f70e228dbf655d8b7137b"},"schema_version":"1.0"},"canonical_sha256":"7f566ad42111e9b28ab542a615b215f2a9e671e7f35bf653c3f8841614cff10f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:40:02.282524Z","signature_b64":"L95rmh788ISjQkyCMDlrlRo8ZLq/tkgo9w/NM/uO7oeT+LAFibHOnClMBACxl/OerVeT1ZR2biUHaNEdssU+BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f566ad42111e9b28ab542a615b215f2a9e671e7f35bf653c3f8841614cff10f","last_reissued_at":"2026-07-05T05:40:02.282180Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:40:02.282180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.04228","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":"2026-07-05T05:40:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ADwchpI91LNiqZN7OPfPzmV4d3ZHy39ePnAp5Fs1M/GJDy3itpPphK4KljghXGeJVMReVGCdeP4mGLOwNJS0DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:29:52.345662Z"},"content_sha256":"9883901e27adb2ceadbcaac9d0b0ed1158e42769ece37ac5f4cbfc4c4f578e82","schema_version":"1.0","event_id":"sha256:9883901e27adb2ceadbcaac9d0b0ed1158e42769ece37ac5f4cbfc4c4f578e82"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:P5LGVVBBCHU3FCVVIKTBLMQV6K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andrew Gelman, Eric P. Xing, Han Guo, Hongyi Wang, Philip Greengard, Yoon Kim","submitted_at":"2023-02-08T17:58:11Z","abstract_excerpt":"The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decomposes across client loss functions. A recent alternative formulation instead treats federated learning as a distributed inference problem, where the goal is to infer a global posterior from partitioned client data (Al-Shedivat et al., 2021). This paper extends the inference view and describes a variational inference formulation of federated learning where the goal is to find a global variational posterior that well-app"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.04228","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/2302.04228/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":"2026-07-05T05:40:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H34wmZeh9+/2+KmgErBZEKXuUkROz4/fx/kguoYI4UiUsgpWO7o7OCqlVc3wzHjKBm+QxiLOwyLr4fB6Ln9vDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:29:52.346164Z"},"content_sha256":"acb07c1713490d7e45814ad42d60c5fd3254d3192e342e50f967d6bc2b4cd300","schema_version":"1.0","event_id":"sha256:acb07c1713490d7e45814ad42d60c5fd3254d3192e342e50f967d6bc2b4cd300"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P5LGVVBBCHU3FCVVIKTBLMQV6K/bundle.json","state_url":"https://pith.science/pith/P5LGVVBBCHU3FCVVIKTBLMQV6K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P5LGVVBBCHU3FCVVIKTBLMQV6K/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":"2026-08-06T16:29:52Z","links":{"resolver":"https://pith.science/pith/P5LGVVBBCHU3FCVVIKTBLMQV6K","bundle":"https://pith.science/pith/P5LGVVBBCHU3FCVVIKTBLMQV6K/bundle.json","state":"https://pith.science/pith/P5LGVVBBCHU3FCVVIKTBLMQV6K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P5LGVVBBCHU3FCVVIKTBLMQV6K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:P5LGVVBBCHU3FCVVIKTBLMQV6K","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":"f00a4e346831175ecbcee5ab131b3eda845b9e0f691f70e228dbf655d8b7137b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-08T17:58:11Z","title_canon_sha256":"ae7840e889b03e726ab5c9f64574710b58d6d1115ac56229b022c141542fb65a"},"schema_version":"1.0","source":{"id":"2302.04228","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.04228","created_at":"2026-07-05T05:40:02Z"},{"alias_kind":"arxiv_version","alias_value":"2302.04228v1","created_at":"2026-07-05T05:40:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.04228","created_at":"2026-07-05T05:40:02Z"},{"alias_kind":"pith_short_12","alias_value":"P5LGVVBBCHU3","created_at":"2026-07-05T05:40:02Z"},{"alias_kind":"pith_short_16","alias_value":"P5LGVVBBCHU3FCVV","created_at":"2026-07-05T05:40:02Z"},{"alias_kind":"pith_short_8","alias_value":"P5LGVVBB","created_at":"2026-07-05T05:40:02Z"}],"graph_snapshots":[{"event_id":"sha256:acb07c1713490d7e45814ad42d60c5fd3254d3192e342e50f967d6bc2b4cd300","target":"graph","created_at":"2026-07-05T05:40:02Z","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/2302.04228/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decomposes across client loss functions. A recent alternative formulation instead treats federated learning as a distributed inference problem, where the goal is to infer a global posterior from partitioned client data (Al-Shedivat et al., 2021). This paper extends the inference view and describes a variational inference formulation of federated learning where the goal is to find a global variational posterior that well-app","authors_text":"Andrew Gelman, Eric P. Xing, Han Guo, Hongyi Wang, Philip Greengard, Yoon Kim","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-08T17:58:11Z","title":"Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.04228","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":"sha256:9883901e27adb2ceadbcaac9d0b0ed1158e42769ece37ac5f4cbfc4c4f578e82","target":"record","created_at":"2026-07-05T05:40:02Z","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":"f00a4e346831175ecbcee5ab131b3eda845b9e0f691f70e228dbf655d8b7137b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-08T17:58:11Z","title_canon_sha256":"ae7840e889b03e726ab5c9f64574710b58d6d1115ac56229b022c141542fb65a"},"schema_version":"1.0","source":{"id":"2302.04228","kind":"arxiv","version":1}},"canonical_sha256":"7f566ad42111e9b28ab542a615b215f2a9e671e7f35bf653c3f8841614cff10f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f566ad42111e9b28ab542a615b215f2a9e671e7f35bf653c3f8841614cff10f","first_computed_at":"2026-07-05T05:40:02.282180Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:40:02.282180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L95rmh788ISjQkyCMDlrlRo8ZLq/tkgo9w/NM/uO7oeT+LAFibHOnClMBACxl/OerVeT1ZR2biUHaNEdssU+BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:40:02.282524Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.04228","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9883901e27adb2ceadbcaac9d0b0ed1158e42769ece37ac5f4cbfc4c4f578e82","sha256:acb07c1713490d7e45814ad42d60c5fd3254d3192e342e50f967d6bc2b4cd300"],"state_sha256":"5f748bd5aa600dd328e67e5c49f38d610becac1375c297f7148b10af3244d1e7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"keN4hJGRmIqPzZ52K5S0SceYqfdIvkpubr8qDqqHGTvIBv4dLTAb1y3rkptXaVwIKquhqU9JHvkkAhOz3+ScAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T16:29:52.349808Z","bundle_sha256":"528ba3ab2ab99e756b22a63d33c3aaa2f0211711256764413056ff0c6e2ee64a"}}