{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:7NBWEWYMYR3H3ZQQ6XNXWOSLKR","short_pith_number":"pith:7NBWEWYM","canonical_record":{"source":{"id":"2306.16703","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-29T05:58:47Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"ad538135730b5fa8bf98d55d246094ecaa384d76d45c715f8da76420e8ae8ff4","abstract_canon_sha256":"319e46a3bde12e0ac7bc0d996fc07ee4e7d05bb67b2b6ae99c100c28bcd62ae2"},"schema_version":"1.0"},"canonical_sha256":"fb43625b0cc4767de610f5db7b3a4b54504fc985c5f1727d28a85b474812ea73","source":{"kind":"arxiv","id":"2306.16703","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.16703","created_at":"2026-07-05T06:38:01Z"},{"alias_kind":"arxiv_version","alias_value":"2306.16703v3","created_at":"2026-07-05T06:38:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.16703","created_at":"2026-07-05T06:38:01Z"},{"alias_kind":"pith_short_12","alias_value":"7NBWEWYMYR3H","created_at":"2026-07-05T06:38:01Z"},{"alias_kind":"pith_short_16","alias_value":"7NBWEWYMYR3H3ZQQ","created_at":"2026-07-05T06:38:01Z"},{"alias_kind":"pith_short_8","alias_value":"7NBWEWYM","created_at":"2026-07-05T06:38:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:7NBWEWYMYR3H3ZQQ6XNXWOSLKR","target":"record","payload":{"canonical_record":{"source":{"id":"2306.16703","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-29T05:58:47Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"ad538135730b5fa8bf98d55d246094ecaa384d76d45c715f8da76420e8ae8ff4","abstract_canon_sha256":"319e46a3bde12e0ac7bc0d996fc07ee4e7d05bb67b2b6ae99c100c28bcd62ae2"},"schema_version":"1.0"},"canonical_sha256":"fb43625b0cc4767de610f5db7b3a4b54504fc985c5f1727d28a85b474812ea73","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:38:01.375452Z","signature_b64":"saXkoI6/84oJj+Wi+l6THJ0OcLAijI6RxhIZi+HqQFwImbGfWhMSvium776Oh8ZsUoRjbk9mr62rI1bO2n3dBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb43625b0cc4767de610f5db7b3a4b54504fc985c5f1727d28a85b474812ea73","last_reissued_at":"2026-07-05T06:38:01.375093Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:38:01.375093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.16703","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:38:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9i+tO0mvORPgTcc7nVcnBja5eFypAC/hdcV30KJSj3+8MoDLoxLK7qL7hQfJTdP3RmLTlGeK9CQoRaNsKXx7Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T23:17:53.269200Z"},"content_sha256":"2a887c71c7c8366cfb876f3c7185f3f9dcb19673dc37697412f3dfcf06cd6a05","schema_version":"1.0","event_id":"sha256:2a887c71c7c8366cfb876f3c7185f3f9dcb19673dc37697412f3dfcf06cd6a05"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:7NBWEWYMYR3H3ZQQ6XNXWOSLKR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Elastically-Constrained Meta-Learner for Federated Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Chong Xie, Donglai Chen, Jinyuan He, Juntao Zhang, Keshu Chen, Peng Lan, Yan Xu, Yonghong Chen","submitted_at":"2023-06-29T05:58:47Z","abstract_excerpt":"Federated learning is an approach to collaboratively training machine learning models for multiple parties that prohibit data sharing. One of the challenges in federated learning is non-IID data between clients, as a single model can not fit the data distribution for all clients. Meta-learning, such as Per-FedAvg, is introduced to cope with the challenge. Meta-learning learns shared initial parameters for all clients. Each client employs gradient descent to adapt the initialization to local data distributions quickly to realize model personalization. However, due to non-convex loss function an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.16703","kind":"arxiv","version":3},"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/2306.16703/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":"2026-07-05T06:38:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qFofyeFlj1vZh1e0cCBdWKHS6U1BxXWdTKta9sA3iM6FdYDa5Kmi8bc3VjKe4WuYway32o3HhwTqDwSHIwg7Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T23:17:53.269713Z"},"content_sha256":"4a58531c9c7401f0b08c5f6fb44adbd297519c465765fdc0d739ce3e19c1008a","schema_version":"1.0","event_id":"sha256:4a58531c9c7401f0b08c5f6fb44adbd297519c465765fdc0d739ce3e19c1008a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7NBWEWYMYR3H3ZQQ6XNXWOSLKR/bundle.json","state_url":"https://pith.science/pith/7NBWEWYMYR3H3ZQQ6XNXWOSLKR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7NBWEWYMYR3H3ZQQ6XNXWOSLKR/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":"2026-08-22T23:17:53Z","links":{"resolver":"https://pith.science/pith/7NBWEWYMYR3H3ZQQ6XNXWOSLKR","bundle":"https://pith.science/pith/7NBWEWYMYR3H3ZQQ6XNXWOSLKR/bundle.json","state":"https://pith.science/pith/7NBWEWYMYR3H3ZQQ6XNXWOSLKR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7NBWEWYMYR3H3ZQQ6XNXWOSLKR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:7NBWEWYMYR3H3ZQQ6XNXWOSLKR","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":"319e46a3bde12e0ac7bc0d996fc07ee4e7d05bb67b2b6ae99c100c28bcd62ae2","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-29T05:58:47Z","title_canon_sha256":"ad538135730b5fa8bf98d55d246094ecaa384d76d45c715f8da76420e8ae8ff4"},"schema_version":"1.0","source":{"id":"2306.16703","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.16703","created_at":"2026-07-05T06:38:01Z"},{"alias_kind":"arxiv_version","alias_value":"2306.16703v3","created_at":"2026-07-05T06:38:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.16703","created_at":"2026-07-05T06:38:01Z"},{"alias_kind":"pith_short_12","alias_value":"7NBWEWYMYR3H","created_at":"2026-07-05T06:38:01Z"},{"alias_kind":"pith_short_16","alias_value":"7NBWEWYMYR3H3ZQQ","created_at":"2026-07-05T06:38:01Z"},{"alias_kind":"pith_short_8","alias_value":"7NBWEWYM","created_at":"2026-07-05T06:38:01Z"}],"graph_snapshots":[{"event_id":"sha256:4a58531c9c7401f0b08c5f6fb44adbd297519c465765fdc0d739ce3e19c1008a","target":"graph","created_at":"2026-07-05T06:38:01Z","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/2306.16703/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning is an approach to collaboratively training machine learning models for multiple parties that prohibit data sharing. One of the challenges in federated learning is non-IID data between clients, as a single model can not fit the data distribution for all clients. Meta-learning, such as Per-FedAvg, is introduced to cope with the challenge. Meta-learning learns shared initial parameters for all clients. Each client employs gradient descent to adapt the initialization to local data distributions quickly to realize model personalization. However, due to non-convex loss function an","authors_text":"Chong Xie, Donglai Chen, Jinyuan He, Juntao Zhang, Keshu Chen, Peng Lan, Yan Xu, Yonghong Chen","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-29T05:58:47Z","title":"Elastically-Constrained Meta-Learner for Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.16703","kind":"arxiv","version":3},"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":"sha256:2a887c71c7c8366cfb876f3c7185f3f9dcb19673dc37697412f3dfcf06cd6a05","target":"record","created_at":"2026-07-05T06:38:01Z","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":"319e46a3bde12e0ac7bc0d996fc07ee4e7d05bb67b2b6ae99c100c28bcd62ae2","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-29T05:58:47Z","title_canon_sha256":"ad538135730b5fa8bf98d55d246094ecaa384d76d45c715f8da76420e8ae8ff4"},"schema_version":"1.0","source":{"id":"2306.16703","kind":"arxiv","version":3}},"canonical_sha256":"fb43625b0cc4767de610f5db7b3a4b54504fc985c5f1727d28a85b474812ea73","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fb43625b0cc4767de610f5db7b3a4b54504fc985c5f1727d28a85b474812ea73","first_computed_at":"2026-07-05T06:38:01.375093Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:38:01.375093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"saXkoI6/84oJj+Wi+l6THJ0OcLAijI6RxhIZi+HqQFwImbGfWhMSvium776Oh8ZsUoRjbk9mr62rI1bO2n3dBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:38:01.375452Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.16703","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2a887c71c7c8366cfb876f3c7185f3f9dcb19673dc37697412f3dfcf06cd6a05","sha256:4a58531c9c7401f0b08c5f6fb44adbd297519c465765fdc0d739ce3e19c1008a"],"state_sha256":"a728842ca181db177eb806becd08e8c8fa8c49f890c17e093cbef58151ef4488"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yHQhirKQv2hXeiZHYVKrn3ODkCO0tKK7+1HvRz8Mvl/D/XORbnNwUKCfYQUouhjzget3wdh6HKccHw6yKlvNCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T23:17:53.274830Z","bundle_sha256":"3e65cbe8f49c009162fa02d620f8730df0e227dfcf2c9889a6b4bc4941091d87"}}