{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:RNAI22Q4Z2BK4MHUUTHP5ULUG6","short_pith_number":"pith:RNAI22Q4","canonical_record":{"source":{"id":"2111.10192","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-19T12:58:59Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"555571e228ee69419496473754ac98e55d434e89c9aa101fd4c062ff5392fce8","abstract_canon_sha256":"749d8958b9afedb9253cfeffec60eb8d4d74a798d0eeeb4f27284fab0a813dee"},"schema_version":"1.0"},"canonical_sha256":"8b408d6a1cce82ae30f4a4cefed17437ae5b247d4c0ecaddeb7943fdf399cab4","source":{"kind":"arxiv","id":"2111.10192","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.10192","created_at":"2026-07-05T03:33:29Z"},{"alias_kind":"arxiv_version","alias_value":"2111.10192v1","created_at":"2026-07-05T03:33:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.10192","created_at":"2026-07-05T03:33:29Z"},{"alias_kind":"pith_short_12","alias_value":"RNAI22Q4Z2BK","created_at":"2026-07-05T03:33:29Z"},{"alias_kind":"pith_short_16","alias_value":"RNAI22Q4Z2BK4MHU","created_at":"2026-07-05T03:33:29Z"},{"alias_kind":"pith_short_8","alias_value":"RNAI22Q4","created_at":"2026-07-05T03:33:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:RNAI22Q4Z2BK4MHUUTHP5ULUG6","target":"record","payload":{"canonical_record":{"source":{"id":"2111.10192","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-19T12:58:59Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"555571e228ee69419496473754ac98e55d434e89c9aa101fd4c062ff5392fce8","abstract_canon_sha256":"749d8958b9afedb9253cfeffec60eb8d4d74a798d0eeeb4f27284fab0a813dee"},"schema_version":"1.0"},"canonical_sha256":"8b408d6a1cce82ae30f4a4cefed17437ae5b247d4c0ecaddeb7943fdf399cab4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:33:29.288788Z","signature_b64":"4xOKTsuA7rYMcw3KViKL9wxrSQKreE2isEDi16Bzrz/Qc0C0X73wHSVzmWULc5hXp/n5Z0q2qvwmNXoyWOacAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b408d6a1cce82ae30f4a4cefed17437ae5b247d4c0ecaddeb7943fdf399cab4","last_reissued_at":"2026-07-05T03:33:29.288272Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:33:29.288272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.10192","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":"2026-07-05T03:33:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i5tSuZSRhsXc2dtKUkQrRR3Ro1Bvr6me265bxQc1SekoKO1rVys/jNNgLm0XpVuxPUYytCiXzskxBVSSrQQkAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:32:20.150713Z"},"content_sha256":"f46dfa20c66ac1d232a0575b37cf8f463b6871c4d8b00aa9a9e0f182f597f680","schema_version":"1.0","event_id":"sha256:f46dfa20c66ac1d232a0575b37cf8f463b6871c4d8b00aa9a9e0f182f597f680"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:RNAI22Q4Z2BK4MHUUTHP5ULUG6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Expectation-Maximization Perspective on Federated Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Christos Louizos, Joseph Soriaga, Matthias Reisser, Max Welling","submitted_at":"2021-11-19T12:58:59Z","abstract_excerpt":"Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated federated learning process as a hierarchical latent variable model where the server provides the parameters of a prior distribution over the client-specific model parameters. We show that with simple Gaussian priors and a hard version of the well known Expectation-Maximization (EM) algorithm, learning in such a model corresponds to FedAvg, the most popular algorithm for the federated learning setting. This perspective on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.10192","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/2111.10192/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":"2026-07-05T03:33:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MJVr95fLKBvT8RHkkkwyic82J+ZSAMSkm1bnf1J0ncIBjuvxCNGe0dwrMga8BKPtiGX50Z0+G1LvkVATot1yAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:32:20.151115Z"},"content_sha256":"df17f0b9c92b6b9c74d297a9438d4341c09414a7e5c855bc380bdceb8d93aba3","schema_version":"1.0","event_id":"sha256:df17f0b9c92b6b9c74d297a9438d4341c09414a7e5c855bc380bdceb8d93aba3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RNAI22Q4Z2BK4MHUUTHP5ULUG6/bundle.json","state_url":"https://pith.science/pith/RNAI22Q4Z2BK4MHUUTHP5ULUG6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RNAI22Q4Z2BK4MHUUTHP5ULUG6/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":"2026-07-06T19:32:20Z","links":{"resolver":"https://pith.science/pith/RNAI22Q4Z2BK4MHUUTHP5ULUG6","bundle":"https://pith.science/pith/RNAI22Q4Z2BK4MHUUTHP5ULUG6/bundle.json","state":"https://pith.science/pith/RNAI22Q4Z2BK4MHUUTHP5ULUG6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RNAI22Q4Z2BK4MHUUTHP5ULUG6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:RNAI22Q4Z2BK4MHUUTHP5ULUG6","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":"749d8958b9afedb9253cfeffec60eb8d4d74a798d0eeeb4f27284fab0a813dee","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-19T12:58:59Z","title_canon_sha256":"555571e228ee69419496473754ac98e55d434e89c9aa101fd4c062ff5392fce8"},"schema_version":"1.0","source":{"id":"2111.10192","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.10192","created_at":"2026-07-05T03:33:29Z"},{"alias_kind":"arxiv_version","alias_value":"2111.10192v1","created_at":"2026-07-05T03:33:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.10192","created_at":"2026-07-05T03:33:29Z"},{"alias_kind":"pith_short_12","alias_value":"RNAI22Q4Z2BK","created_at":"2026-07-05T03:33:29Z"},{"alias_kind":"pith_short_16","alias_value":"RNAI22Q4Z2BK4MHU","created_at":"2026-07-05T03:33:29Z"},{"alias_kind":"pith_short_8","alias_value":"RNAI22Q4","created_at":"2026-07-05T03:33:29Z"}],"graph_snapshots":[{"event_id":"sha256:df17f0b9c92b6b9c74d297a9438d4341c09414a7e5c855bc380bdceb8d93aba3","target":"graph","created_at":"2026-07-05T03:33:29Z","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/2111.10192/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated federated learning process as a hierarchical latent variable model where the server provides the parameters of a prior distribution over the client-specific model parameters. We show that with simple Gaussian priors and a hard version of the well known Expectation-Maximization (EM) algorithm, learning in such a model corresponds to FedAvg, the most popular algorithm for the federated learning setting. This perspective on","authors_text":"Christos Louizos, Joseph Soriaga, Matthias Reisser, Max Welling","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-19T12:58:59Z","title":"An Expectation-Maximization Perspective on Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.10192","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:f46dfa20c66ac1d232a0575b37cf8f463b6871c4d8b00aa9a9e0f182f597f680","target":"record","created_at":"2026-07-05T03:33:29Z","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":"749d8958b9afedb9253cfeffec60eb8d4d74a798d0eeeb4f27284fab0a813dee","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-19T12:58:59Z","title_canon_sha256":"555571e228ee69419496473754ac98e55d434e89c9aa101fd4c062ff5392fce8"},"schema_version":"1.0","source":{"id":"2111.10192","kind":"arxiv","version":1}},"canonical_sha256":"8b408d6a1cce82ae30f4a4cefed17437ae5b247d4c0ecaddeb7943fdf399cab4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8b408d6a1cce82ae30f4a4cefed17437ae5b247d4c0ecaddeb7943fdf399cab4","first_computed_at":"2026-07-05T03:33:29.288272Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:33:29.288272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4xOKTsuA7rYMcw3KViKL9wxrSQKreE2isEDi16Bzrz/Qc0C0X73wHSVzmWULc5hXp/n5Z0q2qvwmNXoyWOacAw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:33:29.288788Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.10192","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f46dfa20c66ac1d232a0575b37cf8f463b6871c4d8b00aa9a9e0f182f597f680","sha256:df17f0b9c92b6b9c74d297a9438d4341c09414a7e5c855bc380bdceb8d93aba3"],"state_sha256":"c602f924f11b30c8e7f6885fa5cf80a35b657b22bee10804375fcbaa818b823e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CIDwvPLLO453asHbHXLBf2Bpl9lSbL9tOmQ4QPi0ZnxQIS9QhnEjVJ4vPGAfvZKNrf07DoP+tqPTrV6ycHf1BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T19:32:20.153097Z","bundle_sha256":"21b09857b2b074f75adaf30d4d1eac39a3598ca0a41b1d86ec38eba0f3237407"}}