{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:L7JCJPIW7TMEMVOTPDST6QD2WZ","short_pith_number":"pith:L7JCJPIW","canonical_record":{"source":{"id":"2411.10619","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-15T22:44:50Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"d38ecb682cf13c2e732ed6aff7cd65b4d1378131c7a2cb8e9b8fcb6d7aa1711a","abstract_canon_sha256":"878753708bf0f321450e0b72b82954fcadf590568e41a61aeae82f4f124b70ef"},"schema_version":"1.0"},"canonical_sha256":"5fd224bd16fcd84655d378e53f407ab66b2fd6fc8a1fee567d57701a342d5ad8","source":{"kind":"arxiv","id":"2411.10619","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.10619","created_at":"2026-07-05T09:36:51Z"},{"alias_kind":"arxiv_version","alias_value":"2411.10619v1","created_at":"2026-07-05T09:36:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.10619","created_at":"2026-07-05T09:36:51Z"},{"alias_kind":"pith_short_12","alias_value":"L7JCJPIW7TME","created_at":"2026-07-05T09:36:51Z"},{"alias_kind":"pith_short_16","alias_value":"L7JCJPIW7TMEMVOT","created_at":"2026-07-05T09:36:51Z"},{"alias_kind":"pith_short_8","alias_value":"L7JCJPIW","created_at":"2026-07-05T09:36:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:L7JCJPIW7TMEMVOTPDST6QD2WZ","target":"record","payload":{"canonical_record":{"source":{"id":"2411.10619","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-15T22:44:50Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"d38ecb682cf13c2e732ed6aff7cd65b4d1378131c7a2cb8e9b8fcb6d7aa1711a","abstract_canon_sha256":"878753708bf0f321450e0b72b82954fcadf590568e41a61aeae82f4f124b70ef"},"schema_version":"1.0"},"canonical_sha256":"5fd224bd16fcd84655d378e53f407ab66b2fd6fc8a1fee567d57701a342d5ad8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:51.401365Z","signature_b64":"9SBxtddQSMNARQX4+pYQfb6mLq9brFQ7aA26Ei/CeFfH3KmlzuZ4hecIz5julZdH83zTfCK9uYh1SLx6vznXDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fd224bd16fcd84655d378e53f407ab66b2fd6fc8a1fee567d57701a342d5ad8","last_reissued_at":"2026-07-05T09:36:51.400876Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:51.400876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.10619","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":"2026-07-05T09:36:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gpCLMfOjaROzphFhZW0kdLO9ks9lDVe4hX3LnGDesXburD46SpwNeuIJ1afdgjt8U18Dg58RLln7P3d6OlNyBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T06:01:48.141046Z"},"content_sha256":"ddd5adb1f53cbf9342a50f4deac1357098027e1a0602fcb9a6046a1ab4e66e0c","schema_version":"1.0","event_id":"sha256:ddd5adb1f53cbf9342a50f4deac1357098027e1a0602fcb9a6046a1ab4e66e0c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:L7JCJPIW7TMEMVOTPDST6QD2WZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Dinh C. Nguyen, Neeraj Kumar, Ratun Rahman","submitted_at":"2024-11-15T22:44:50Z","abstract_excerpt":"Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) are used to record household energy consumption. Traditional machine learning (ML) methods are often employed for load forecasting but require data sharing which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.10619","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/2411.10619/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":"2026-07-05T09:36:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gefZUDCqT3+GZeIyAw7AvobTsmxU2VmKsYcuJgLKdgcP4oCfioJ0AUOLemSKlFR22GD2M61gA6WOeTk3nl2TCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T06:01:48.141540Z"},"content_sha256":"7d821d7c363921d99060ffe3f47735dc99f2c097cc630b0dbcf7900eb5856de6","schema_version":"1.0","event_id":"sha256:7d821d7c363921d99060ffe3f47735dc99f2c097cc630b0dbcf7900eb5856de6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L7JCJPIW7TMEMVOTPDST6QD2WZ/bundle.json","state_url":"https://pith.science/pith/L7JCJPIW7TMEMVOTPDST6QD2WZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L7JCJPIW7TMEMVOTPDST6QD2WZ/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":"2026-08-14T06:01:48Z","links":{"resolver":"https://pith.science/pith/L7JCJPIW7TMEMVOTPDST6QD2WZ","bundle":"https://pith.science/pith/L7JCJPIW7TMEMVOTPDST6QD2WZ/bundle.json","state":"https://pith.science/pith/L7JCJPIW7TMEMVOTPDST6QD2WZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L7JCJPIW7TMEMVOTPDST6QD2WZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:L7JCJPIW7TMEMVOTPDST6QD2WZ","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":"878753708bf0f321450e0b72b82954fcadf590568e41a61aeae82f4f124b70ef","cross_cats_sorted":["eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-15T22:44:50Z","title_canon_sha256":"d38ecb682cf13c2e732ed6aff7cd65b4d1378131c7a2cb8e9b8fcb6d7aa1711a"},"schema_version":"1.0","source":{"id":"2411.10619","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.10619","created_at":"2026-07-05T09:36:51Z"},{"alias_kind":"arxiv_version","alias_value":"2411.10619v1","created_at":"2026-07-05T09:36:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.10619","created_at":"2026-07-05T09:36:51Z"},{"alias_kind":"pith_short_12","alias_value":"L7JCJPIW7TME","created_at":"2026-07-05T09:36:51Z"},{"alias_kind":"pith_short_16","alias_value":"L7JCJPIW7TMEMVOT","created_at":"2026-07-05T09:36:51Z"},{"alias_kind":"pith_short_8","alias_value":"L7JCJPIW","created_at":"2026-07-05T09:36:51Z"}],"graph_snapshots":[{"event_id":"sha256:7d821d7c363921d99060ffe3f47735dc99f2c097cc630b0dbcf7900eb5856de6","target":"graph","created_at":"2026-07-05T09:36:51Z","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/2411.10619/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) are used to record household energy consumption. Traditional machine learning (ML) methods are often employed for load forecasting but require data sharing which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribu","authors_text":"Dinh C. Nguyen, Neeraj Kumar, Ratun Rahman","cross_cats":["eess.SP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-15T22:44:50Z","title":"Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.10619","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:ddd5adb1f53cbf9342a50f4deac1357098027e1a0602fcb9a6046a1ab4e66e0c","target":"record","created_at":"2026-07-05T09:36:51Z","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":"878753708bf0f321450e0b72b82954fcadf590568e41a61aeae82f4f124b70ef","cross_cats_sorted":["eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-15T22:44:50Z","title_canon_sha256":"d38ecb682cf13c2e732ed6aff7cd65b4d1378131c7a2cb8e9b8fcb6d7aa1711a"},"schema_version":"1.0","source":{"id":"2411.10619","kind":"arxiv","version":1}},"canonical_sha256":"5fd224bd16fcd84655d378e53f407ab66b2fd6fc8a1fee567d57701a342d5ad8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5fd224bd16fcd84655d378e53f407ab66b2fd6fc8a1fee567d57701a342d5ad8","first_computed_at":"2026-07-05T09:36:51.400876Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:36:51.400876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9SBxtddQSMNARQX4+pYQfb6mLq9brFQ7aA26Ei/CeFfH3KmlzuZ4hecIz5julZdH83zTfCK9uYh1SLx6vznXDg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:36:51.401365Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.10619","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ddd5adb1f53cbf9342a50f4deac1357098027e1a0602fcb9a6046a1ab4e66e0c","sha256:7d821d7c363921d99060ffe3f47735dc99f2c097cc630b0dbcf7900eb5856de6"],"state_sha256":"58e2063fb104dc32494b03b0cb010f3f5822c0b84cfc583b3dd0d2a9c60d7622"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Yk9qDsQ9zlZsu3Zw0LQRJbonnECG8L7alJ+EalTD6Fnnp20DQzLiF9NBGWrszFqbAMYGp0cktCpoCc+VRWdGCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T06:01:48.146329Z","bundle_sha256":"caf606fc6dbb4b2ba79acfb62f385ea0bd32fbbb676e96579b15b6c282477138"}}