{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:Q3DNNZBAGOCW4RERGWBCZCUKFE","short_pith_number":"pith:Q3DNNZBA","canonical_record":{"source":{"id":"2107.06724","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-14T14:15:24Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"9314a63689b2a8dfddc0bc0c67f142dd5170ccddab2cbec74391427cc05bd697","abstract_canon_sha256":"39f67a7fc474b312919512d61b7ebd90e67a873a5f1b2734913ab8f3f434b5ff"},"schema_version":"1.0"},"canonical_sha256":"86c6d6e42033856e449135822c8a8a292e88954f67a46f8645dd5839bbc2ca90","source":{"kind":"arxiv","id":"2107.06724","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.06724","created_at":"2026-07-05T02:58:00Z"},{"alias_kind":"arxiv_version","alias_value":"2107.06724v1","created_at":"2026-07-05T02:58:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.06724","created_at":"2026-07-05T02:58:00Z"},{"alias_kind":"pith_short_12","alias_value":"Q3DNNZBAGOCW","created_at":"2026-07-05T02:58:00Z"},{"alias_kind":"pith_short_16","alias_value":"Q3DNNZBAGOCW4RER","created_at":"2026-07-05T02:58:00Z"},{"alias_kind":"pith_short_8","alias_value":"Q3DNNZBA","created_at":"2026-07-05T02:58:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:Q3DNNZBAGOCW4RERGWBCZCUKFE","target":"record","payload":{"canonical_record":{"source":{"id":"2107.06724","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-14T14:15:24Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"9314a63689b2a8dfddc0bc0c67f142dd5170ccddab2cbec74391427cc05bd697","abstract_canon_sha256":"39f67a7fc474b312919512d61b7ebd90e67a873a5f1b2734913ab8f3f434b5ff"},"schema_version":"1.0"},"canonical_sha256":"86c6d6e42033856e449135822c8a8a292e88954f67a46f8645dd5839bbc2ca90","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:58:00.865416Z","signature_b64":"6674UlV3clMVP04JCBcXzQLbY75ZX0hMOqRaOLMNsy6br8eqv/lJo7OvEXENaAGz28/2vdq6/BiTkLK6R7ZFDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86c6d6e42033856e449135822c8a8a292e88954f67a46f8645dd5839bbc2ca90","last_reissued_at":"2026-07-05T02:58:00.864956Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:58:00.864956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.06724","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":"2026-07-05T02:58:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4P/KU0vMTgkWQyk0bQ0/8Sxsy0Ii235zGjYQX3PnoNGyA7SPQGqT/dZ8T9WWIUZph5LyR8V/YHTS3Mzx/bt8BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:32:12.719266Z"},"content_sha256":"0601ff6e4839a3fd05934322e030fd52220e11715badb15666d6b4aa653e9ceb","schema_version":"1.0","event_id":"sha256:0601ff6e4839a3fd05934322e030fd52220e11715badb15666d6b4aa653e9ceb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:Q3DNNZBAGOCW4RERGWBCZCUKFE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Federated Mixture of Experts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Christos Louizos, Efstratios Gavves, Matthias Reisser, Max Welling","submitted_at":"2021-07-14T14:15:24Z","abstract_excerpt":"Federated learning (FL) has emerged as the predominant approach for collaborative training of neural network models across multiple users, without the need to gather the data at a central location. One of the important challenges in this setting is data heterogeneity, i.e. different users have different data characteristics. For this reason, training and using a single global model might be suboptimal when considering the performance of each of the individual user's data. In this work, we tackle this problem via Federated Mixture of Experts, FedMix, a framework that allows us to train an ensem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.06724","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/2107.06724/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":"2026-07-05T02:58:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GMjg8epR7VFGoxv5W+0ajDXgQtDsDVdr1GOV9gTdW2v4+8TxXKA3I2QQUL0Byc5vc0voQnMwiUI5BCV2BB9nDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:32:12.719654Z"},"content_sha256":"a1d49a4819e64cfb99b2fb4536563187830cd2d6805ce90e2e2c7f17c8d3581c","schema_version":"1.0","event_id":"sha256:a1d49a4819e64cfb99b2fb4536563187830cd2d6805ce90e2e2c7f17c8d3581c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q3DNNZBAGOCW4RERGWBCZCUKFE/bundle.json","state_url":"https://pith.science/pith/Q3DNNZBAGOCW4RERGWBCZCUKFE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q3DNNZBAGOCW4RERGWBCZCUKFE/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":"2026-07-06T23:32:12Z","links":{"resolver":"https://pith.science/pith/Q3DNNZBAGOCW4RERGWBCZCUKFE","bundle":"https://pith.science/pith/Q3DNNZBAGOCW4RERGWBCZCUKFE/bundle.json","state":"https://pith.science/pith/Q3DNNZBAGOCW4RERGWBCZCUKFE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q3DNNZBAGOCW4RERGWBCZCUKFE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:Q3DNNZBAGOCW4RERGWBCZCUKFE","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":"39f67a7fc474b312919512d61b7ebd90e67a873a5f1b2734913ab8f3f434b5ff","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-14T14:15:24Z","title_canon_sha256":"9314a63689b2a8dfddc0bc0c67f142dd5170ccddab2cbec74391427cc05bd697"},"schema_version":"1.0","source":{"id":"2107.06724","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.06724","created_at":"2026-07-05T02:58:00Z"},{"alias_kind":"arxiv_version","alias_value":"2107.06724v1","created_at":"2026-07-05T02:58:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.06724","created_at":"2026-07-05T02:58:00Z"},{"alias_kind":"pith_short_12","alias_value":"Q3DNNZBAGOCW","created_at":"2026-07-05T02:58:00Z"},{"alias_kind":"pith_short_16","alias_value":"Q3DNNZBAGOCW4RER","created_at":"2026-07-05T02:58:00Z"},{"alias_kind":"pith_short_8","alias_value":"Q3DNNZBA","created_at":"2026-07-05T02:58:00Z"}],"graph_snapshots":[{"event_id":"sha256:a1d49a4819e64cfb99b2fb4536563187830cd2d6805ce90e2e2c7f17c8d3581c","target":"graph","created_at":"2026-07-05T02:58:00Z","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/2107.06724/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning (FL) has emerged as the predominant approach for collaborative training of neural network models across multiple users, without the need to gather the data at a central location. One of the important challenges in this setting is data heterogeneity, i.e. different users have different data characteristics. For this reason, training and using a single global model might be suboptimal when considering the performance of each of the individual user's data. In this work, we tackle this problem via Federated Mixture of Experts, FedMix, a framework that allows us to train an ensem","authors_text":"Christos Louizos, Efstratios Gavves, Matthias Reisser, Max Welling","cross_cats":["cs.DC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-14T14:15:24Z","title":"Federated Mixture of Experts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.06724","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":"sha256:0601ff6e4839a3fd05934322e030fd52220e11715badb15666d6b4aa653e9ceb","target":"record","created_at":"2026-07-05T02:58:00Z","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":"39f67a7fc474b312919512d61b7ebd90e67a873a5f1b2734913ab8f3f434b5ff","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-14T14:15:24Z","title_canon_sha256":"9314a63689b2a8dfddc0bc0c67f142dd5170ccddab2cbec74391427cc05bd697"},"schema_version":"1.0","source":{"id":"2107.06724","kind":"arxiv","version":1}},"canonical_sha256":"86c6d6e42033856e449135822c8a8a292e88954f67a46f8645dd5839bbc2ca90","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"86c6d6e42033856e449135822c8a8a292e88954f67a46f8645dd5839bbc2ca90","first_computed_at":"2026-07-05T02:58:00.864956Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:58:00.864956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6674UlV3clMVP04JCBcXzQLbY75ZX0hMOqRaOLMNsy6br8eqv/lJo7OvEXENaAGz28/2vdq6/BiTkLK6R7ZFDA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:58:00.865416Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.06724","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0601ff6e4839a3fd05934322e030fd52220e11715badb15666d6b4aa653e9ceb","sha256:a1d49a4819e64cfb99b2fb4536563187830cd2d6805ce90e2e2c7f17c8d3581c"],"state_sha256":"54921c1b8b8734f2564872a741239569015b6d8d0872aa479d1f75f5d4481227"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iAAxFn/8Gy8J9L8V37neL/AM+S5X5Fwcav94Ovkx0Fu+woGVOc4KkXosnECJvlWUXKbvGBq7+09np7xn7S9zBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T23:32:12.721610Z","bundle_sha256":"820032bef0c634a00e9c095b2f99aabcf805a2407fb3e3893cbc4c13553656ad"}}