{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:XPS5TQDKU2KTY6GMZDRK3VIYC7","short_pith_number":"pith:XPS5TQDK","schema_version":"1.0","canonical_sha256":"bbe5d9c06aa6953c78ccc8e2add51817ce941e762cae383ca82d380ca4ad283d","source":{"kind":"arxiv","id":"2307.00233","version":1},"attestation_state":"computed","paper":{"title":"Hierarchical Federated Learning Incentivization for Gas Usage Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Chengyi Yang, Han Yu, Has Sun, Qijie Ding, Xiaoli Tang, Xiuli Wang, Zengxiang Li, Zhenpeng Yu","submitted_at":"2023-07-01T05:45:23Z","abstract_excerpt":"Accurately estimating gas usage is essential for the efficient functioning of gas distribution networks and saving operational costs. Traditional methods rely on centralized data processing, which poses privacy risks. Federated learning (FL) offers a solution to this problem by enabling local data processing on each participant, such as gas companies and heating stations. However, local training and communication overhead may discourage gas companies and heating stations from actively participating in the FL training process. To address this challenge, we propose a Hierarchical FL Incentive Me"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2307.00233","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-01T05:45:23Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"02c0daad464f67f71f22a3eb03556ef3161fb794766c2f4bfdeefa460f3c657b","abstract_canon_sha256":"7906db016408b47fa2c9f6c87a6589e1a9285d04f2bec2eb49b9ec0dfb6203ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:26:53.441469Z","signature_b64":"h7jtxyhTUytOa6IQXbRhDWODDoklR6Fc/s48c4fveWYAtgi40SbMaNYSO5Zbl0YLDsNDxcXogmOVRyG03lCVBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bbe5d9c06aa6953c78ccc8e2add51817ce941e762cae383ca82d380ca4ad283d","last_reissued_at":"2026-07-05T06:26:53.441034Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:26:53.441034Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Federated Learning Incentivization for Gas Usage Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Chengyi Yang, Han Yu, Has Sun, Qijie Ding, Xiaoli Tang, Xiuli Wang, Zengxiang Li, Zhenpeng Yu","submitted_at":"2023-07-01T05:45:23Z","abstract_excerpt":"Accurately estimating gas usage is essential for the efficient functioning of gas distribution networks and saving operational costs. Traditional methods rely on centralized data processing, which poses privacy risks. Federated learning (FL) offers a solution to this problem by enabling local data processing on each participant, such as gas companies and heating stations. However, local training and communication overhead may discourage gas companies and heating stations from actively participating in the FL training process. To address this challenge, we propose a Hierarchical FL Incentive Me"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.00233","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/2307.00233/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2307.00233","created_at":"2026-07-05T06:26:53.441098+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.00233v1","created_at":"2026-07-05T06:26:53.441098+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.00233","created_at":"2026-07-05T06:26:53.441098+00:00"},{"alias_kind":"pith_short_12","alias_value":"XPS5TQDKU2KT","created_at":"2026-07-05T06:26:53.441098+00:00"},{"alias_kind":"pith_short_16","alias_value":"XPS5TQDKU2KTY6GM","created_at":"2026-07-05T06:26:53.441098+00:00"},{"alias_kind":"pith_short_8","alias_value":"XPS5TQDK","created_at":"2026-07-05T06:26:53.441098+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.22192","citing_title":"Efficient Leave-one-out Approximation in LLM Multi-agent Debate Based on Introspection","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XPS5TQDKU2KTY6GMZDRK3VIYC7","json":"https://pith.science/pith/XPS5TQDKU2KTY6GMZDRK3VIYC7.json","graph_json":"https://pith.science/api/pith-number/XPS5TQDKU2KTY6GMZDRK3VIYC7/graph.json","events_json":"https://pith.science/api/pith-number/XPS5TQDKU2KTY6GMZDRK3VIYC7/events.json","paper":"https://pith.science/paper/XPS5TQDK"},"agent_actions":{"view_html":"https://pith.science/pith/XPS5TQDKU2KTY6GMZDRK3VIYC7","download_json":"https://pith.science/pith/XPS5TQDKU2KTY6GMZDRK3VIYC7.json","view_paper":"https://pith.science/paper/XPS5TQDK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.00233&json=true","fetch_graph":"https://pith.science/api/pith-number/XPS5TQDKU2KTY6GMZDRK3VIYC7/graph.json","fetch_events":"https://pith.science/api/pith-number/XPS5TQDKU2KTY6GMZDRK3VIYC7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XPS5TQDKU2KTY6GMZDRK3VIYC7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XPS5TQDKU2KTY6GMZDRK3VIYC7/action/storage_attestation","attest_author":"https://pith.science/pith/XPS5TQDKU2KTY6GMZDRK3VIYC7/action/author_attestation","sign_citation":"https://pith.science/pith/XPS5TQDKU2KTY6GMZDRK3VIYC7/action/citation_signature","submit_replication":"https://pith.science/pith/XPS5TQDKU2KTY6GMZDRK3VIYC7/action/replication_record"}},"created_at":"2026-07-05T06:26:53.441098+00:00","updated_at":"2026-07-05T06:26:53.441098+00:00"}