{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:JZIMXY4LFDNE6FKWFPHRDBZ3LN","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":"8b3bd9280c9889dce99c1a7842f7d280481cd3f76457c6969b0bd4a4285dfc97","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2018-12-10T05:43:42Z","title_canon_sha256":"121e49424c6e6f50c9ed10b6cbab79258546aa75031336028a05191be3199597"},"schema_version":"1.0","source":{"id":"1812.03633","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.03633","created_at":"2026-05-17T23:58:44Z"},{"alias_kind":"arxiv_version","alias_value":"1812.03633v1","created_at":"2026-05-17T23:58:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.03633","created_at":"2026-05-17T23:58:44Z"},{"alias_kind":"pith_short_12","alias_value":"JZIMXY4LFDNE","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"JZIMXY4LFDNE6FKW","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"JZIMXY4L","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:7464a9ec813c17deb8c6be2a52b91cf5ce28dc756c29d765fd4485c7f08acf2e","target":"graph","created_at":"2026-05-17T23:58:44Z","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"},"paper":{"abstract_excerpt":"In this letter, we consider the concept of Mobile Crowd-Machine Learning (MCML) for a federated learning model. The MCML enables mobile devices in a mobile network to collaboratively train neural network models required by a server while keeping data on the mobile devices. The MCML thus addresses data privacy issues of traditional machine learning. However, the mobile devices are constrained by energy, CPU, and wireless bandwidth. Thus, to minimize the energy consumption, training time and communication cost, the server needs to determine proper amounts of data and energy that the mobile devic","authors_text":"Dong In Kim, Dusit Niyato, Li-Chun Wang, Nguyen Cong Luong, Tran The Anh","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2018-12-10T05:43:42Z","title":"Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.03633","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:c05716c87d5f00445ca480b08c17584a5c24371772ed42367db02196a3c5e83f","target":"record","created_at":"2026-05-17T23:58:44Z","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":"8b3bd9280c9889dce99c1a7842f7d280481cd3f76457c6969b0bd4a4285dfc97","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2018-12-10T05:43:42Z","title_canon_sha256":"121e49424c6e6f50c9ed10b6cbab79258546aa75031336028a05191be3199597"},"schema_version":"1.0","source":{"id":"1812.03633","kind":"arxiv","version":1}},"canonical_sha256":"4e50cbe38b28da4f15562bcf11873b5b60d5ac46b9e3c3b294c3e7313bf60d03","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4e50cbe38b28da4f15562bcf11873b5b60d5ac46b9e3c3b294c3e7313bf60d03","first_computed_at":"2026-05-17T23:58:44.266481Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:44.266481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1kUrxoTW1cEqBqNMa7PKFxetFDBMgiZgK1goYrjdmOCpSnEMn2BKYOrxlR3/4NfTZdN6z3MiWOQHwHPjLuvcDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:44.266984Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.03633","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c05716c87d5f00445ca480b08c17584a5c24371772ed42367db02196a3c5e83f","sha256:7464a9ec813c17deb8c6be2a52b91cf5ce28dc756c29d765fd4485c7f08acf2e"],"state_sha256":"665b52f6ab110e3a588d1a4e9fe995d035bee89b24bd58ee88abd4d85d185a25"}