{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:WFKQITJ7FNNF7Z7OBF63TGG3TR","short_pith_number":"pith:WFKQITJ7","canonical_record":{"source":{"id":"2311.11342","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-19T14:56:26Z","cross_cats_sorted":["cs.DC","math.OC"],"title_canon_sha256":"59e1c33536623e8cffe2cbf421321195d999f909949ec7d07530b2c8855ec3b6","abstract_canon_sha256":"0e813635174568016bb474bbbc6e486a5a794d81468f0dd9b8f463fb1480a285"},"schema_version":"1.0"},"canonical_sha256":"b155044d3f2b5a5fe7ee097db998db9c57652ff04813cfc234bdbe18cb7dcbff","source":{"kind":"arxiv","id":"2311.11342","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.11342","created_at":"2026-05-26T02:04:58Z"},{"alias_kind":"arxiv_version","alias_value":"2311.11342v5","created_at":"2026-05-26T02:04:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.11342","created_at":"2026-05-26T02:04:58Z"},{"alias_kind":"pith_short_12","alias_value":"WFKQITJ7FNNF","created_at":"2026-05-26T02:04:58Z"},{"alias_kind":"pith_short_16","alias_value":"WFKQITJ7FNNF7Z7O","created_at":"2026-05-26T02:04:58Z"},{"alias_kind":"pith_short_8","alias_value":"WFKQITJ7","created_at":"2026-05-26T02:04:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:WFKQITJ7FNNF7Z7OBF63TGG3TR","target":"record","payload":{"canonical_record":{"source":{"id":"2311.11342","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-19T14:56:26Z","cross_cats_sorted":["cs.DC","math.OC"],"title_canon_sha256":"59e1c33536623e8cffe2cbf421321195d999f909949ec7d07530b2c8855ec3b6","abstract_canon_sha256":"0e813635174568016bb474bbbc6e486a5a794d81468f0dd9b8f463fb1480a285"},"schema_version":"1.0"},"canonical_sha256":"b155044d3f2b5a5fe7ee097db998db9c57652ff04813cfc234bdbe18cb7dcbff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:04:58.185737Z","signature_b64":"h8DTbIJr53AvmoU0kh5KqJjE2CUb1Z+RoRoR0lpaqTvsA4X4aGfqxaSRlGUkAVMn8etMeAsgot6ivQsxNHZ0Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b155044d3f2b5a5fe7ee097db998db9c57652ff04813cfc234bdbe18cb7dcbff","last_reissued_at":"2026-05-26T02:04:58.184993Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:04:58.184993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.11342","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T02:04:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7Lo94MfPvWWiZmrxF21Vh16fMXtZ6QNliNgVBfDd9A7RHyLBUMpKCC7GVY//dXLVIiK07WATq2vWgXvngKzOAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T14:49:59.254087Z"},"content_sha256":"b5bbb3bbfca8cc8d7a185c56c3a3878a9529393ca9f40b176e00ee19fa9a897c","schema_version":"1.0","event_id":"sha256:b5bbb3bbfca8cc8d7a185c56c3a3878a9529393ca9f40b176e00ee19fa9a897c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:WFKQITJ7FNNF7Z7OBF63TGG3TR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On the Communication Complexity of Decentralized Stochastic Bilevel Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","math.OC"],"primary_cat":"cs.LG","authors_text":"Hongchang Gao, Jie Wu, My T. Thai, Yihan Zhang","submitted_at":"2023-11-19T14:56:26Z","abstract_excerpt":"Stochastic bilevel optimization finds widespread applications in machine learning, including meta-learning, hyperparameter optimization, and neural architecture search. To extend stochastic bilevel optimization to distributed data, several decentralized stochastic bilevel optimization algorithms have been developed. However, existing methods often suffer from slow convergence rates and high communication costs in heterogeneous settings, limiting their applicability to real-world tasks. To address these issues, we propose two novel decentralized stochastic bilevel gradient descent algorithms ba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.11342","kind":"arxiv","version":5},"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/2311.11342/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":"2026-05-26T02:04:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I0JuaWrDIAQI2oFgoH/Vd9SmVRkDZrll22u0qx8z6WdXGh8nxg8G6V/fA0LkLBnuPe6O2qU6hsVFdidDrOP7BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T14:49:59.254824Z"},"content_sha256":"aa9a9fad6f1ce1cb5d3f25e5892977ff8f9374b13d0550d0df5ad492edbeb639","schema_version":"1.0","event_id":"sha256:aa9a9fad6f1ce1cb5d3f25e5892977ff8f9374b13d0550d0df5ad492edbeb639"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WFKQITJ7FNNF7Z7OBF63TGG3TR/bundle.json","state_url":"https://pith.science/pith/WFKQITJ7FNNF7Z7OBF63TGG3TR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WFKQITJ7FNNF7Z7OBF63TGG3TR/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":"2026-05-29T14:49:59Z","links":{"resolver":"https://pith.science/pith/WFKQITJ7FNNF7Z7OBF63TGG3TR","bundle":"https://pith.science/pith/WFKQITJ7FNNF7Z7OBF63TGG3TR/bundle.json","state":"https://pith.science/pith/WFKQITJ7FNNF7Z7OBF63TGG3TR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WFKQITJ7FNNF7Z7OBF63TGG3TR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:WFKQITJ7FNNF7Z7OBF63TGG3TR","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":"0e813635174568016bb474bbbc6e486a5a794d81468f0dd9b8f463fb1480a285","cross_cats_sorted":["cs.DC","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-19T14:56:26Z","title_canon_sha256":"59e1c33536623e8cffe2cbf421321195d999f909949ec7d07530b2c8855ec3b6"},"schema_version":"1.0","source":{"id":"2311.11342","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.11342","created_at":"2026-05-26T02:04:58Z"},{"alias_kind":"arxiv_version","alias_value":"2311.11342v5","created_at":"2026-05-26T02:04:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.11342","created_at":"2026-05-26T02:04:58Z"},{"alias_kind":"pith_short_12","alias_value":"WFKQITJ7FNNF","created_at":"2026-05-26T02:04:58Z"},{"alias_kind":"pith_short_16","alias_value":"WFKQITJ7FNNF7Z7O","created_at":"2026-05-26T02:04:58Z"},{"alias_kind":"pith_short_8","alias_value":"WFKQITJ7","created_at":"2026-05-26T02:04:58Z"}],"graph_snapshots":[{"event_id":"sha256:aa9a9fad6f1ce1cb5d3f25e5892977ff8f9374b13d0550d0df5ad492edbeb639","target":"graph","created_at":"2026-05-26T02:04:58Z","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/2311.11342/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Stochastic bilevel optimization finds widespread applications in machine learning, including meta-learning, hyperparameter optimization, and neural architecture search. To extend stochastic bilevel optimization to distributed data, several decentralized stochastic bilevel optimization algorithms have been developed. However, existing methods often suffer from slow convergence rates and high communication costs in heterogeneous settings, limiting their applicability to real-world tasks. To address these issues, we propose two novel decentralized stochastic bilevel gradient descent algorithms ba","authors_text":"Hongchang Gao, Jie Wu, My T. Thai, Yihan Zhang","cross_cats":["cs.DC","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-19T14:56:26Z","title":"On the Communication Complexity of Decentralized Stochastic Bilevel Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.11342","kind":"arxiv","version":5},"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:b5bbb3bbfca8cc8d7a185c56c3a3878a9529393ca9f40b176e00ee19fa9a897c","target":"record","created_at":"2026-05-26T02:04:58Z","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":"0e813635174568016bb474bbbc6e486a5a794d81468f0dd9b8f463fb1480a285","cross_cats_sorted":["cs.DC","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-19T14:56:26Z","title_canon_sha256":"59e1c33536623e8cffe2cbf421321195d999f909949ec7d07530b2c8855ec3b6"},"schema_version":"1.0","source":{"id":"2311.11342","kind":"arxiv","version":5}},"canonical_sha256":"b155044d3f2b5a5fe7ee097db998db9c57652ff04813cfc234bdbe18cb7dcbff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b155044d3f2b5a5fe7ee097db998db9c57652ff04813cfc234bdbe18cb7dcbff","first_computed_at":"2026-05-26T02:04:58.184993Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T02:04:58.184993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"h8DTbIJr53AvmoU0kh5KqJjE2CUb1Z+RoRoR0lpaqTvsA4X4aGfqxaSRlGUkAVMn8etMeAsgot6ivQsxNHZ0Aw==","signature_status":"signed_v1","signed_at":"2026-05-26T02:04:58.185737Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.11342","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b5bbb3bbfca8cc8d7a185c56c3a3878a9529393ca9f40b176e00ee19fa9a897c","sha256:aa9a9fad6f1ce1cb5d3f25e5892977ff8f9374b13d0550d0df5ad492edbeb639"],"state_sha256":"5933108b39239ace18dbab9d184ea7a31fa1e67dac4b2829c4fba8a68535a785"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xR03E9goNmSodboYcpf6Q3+G94AeepGY//zLrf2hRZ4VioD+Bwrldlvzt5u2I8ZChDt05iM5R0bH7RuE5WFsDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T14:49:59.258727Z","bundle_sha256":"fce1716f0346c41cd31c2b8c5de048a63c07dba70a2cb5ba31cc977d65171c18"}}