{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:2R5V25T4SEZGDRF5C7FNNHWTTF","short_pith_number":"pith:2R5V25T4","schema_version":"1.0","canonical_sha256":"d47b5d767c913261c4bd17cad69ed399588da574440c3aede7a0b84b44085319","source":{"kind":"arxiv","id":"2006.03594","version":3},"attestation_state":"computed","paper":{"title":"From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NI","stat.ML"],"primary_cat":"cs.DC","authors_text":"Christopher G. Brinton, Huaiyu Dai, Mung Chiang, Seyyedali Hosseinalipour, Vaneet Aggarwal","submitted_at":"2020-06-07T05:11:18Z","abstract_excerpt":"Machine learning (ML) tasks are becoming ubiquitous in today's network applications. Federated learning has emerged recently as a technique for training ML models at the network edge by leveraging processing capabilities across the nodes that collect the data. There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices. To address this, we advocate a new learning paradigm called fog learning which will intelligently distribute ML model training acro"},"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":"2006.03594","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-06-07T05:11:18Z","cross_cats_sorted":["cs.LG","cs.NI","stat.ML"],"title_canon_sha256":"9911e02c6d2f7d29521a3f0440fc4a1c721e36312fb2ce0fdb9f6b455f0b52c5","abstract_canon_sha256":"b4a75dca7e1a398446b59f33ec187e0185dd4c64e316c78705264f9f38741b0a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:45:27.345747Z","signature_b64":"EY2blyCYYtvM5NUZnJ8AxYdK+pAnPQ3mWqOFEg+JqOj1ryVuSdv1XmkiigiK70bJOPjT8D9Zf0hr5JFqySNeBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d47b5d767c913261c4bd17cad69ed399588da574440c3aede7a0b84b44085319","last_reissued_at":"2026-07-05T01:45:27.345237Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:45:27.345237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NI","stat.ML"],"primary_cat":"cs.DC","authors_text":"Christopher G. Brinton, Huaiyu Dai, Mung Chiang, Seyyedali Hosseinalipour, Vaneet Aggarwal","submitted_at":"2020-06-07T05:11:18Z","abstract_excerpt":"Machine learning (ML) tasks are becoming ubiquitous in today's network applications. Federated learning has emerged recently as a technique for training ML models at the network edge by leveraging processing capabilities across the nodes that collect the data. There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices. To address this, we advocate a new learning paradigm called fog learning which will intelligently distribute ML model training acro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.03594","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/2006.03594/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":"2006.03594","created_at":"2026-07-05T01:45:27.345297+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.03594v3","created_at":"2026-07-05T01:45:27.345297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.03594","created_at":"2026-07-05T01:45:27.345297+00:00"},{"alias_kind":"pith_short_12","alias_value":"2R5V25T4SEZG","created_at":"2026-07-05T01:45:27.345297+00:00"},{"alias_kind":"pith_short_16","alias_value":"2R5V25T4SEZGDRF5","created_at":"2026-07-05T01:45:27.345297+00:00"},{"alias_kind":"pith_short_8","alias_value":"2R5V25T4","created_at":"2026-07-05T01:45:27.345297+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21416","citing_title":"Bridging Design and Execution: A Visual Graph Editor for Edge and Cloud Workflows","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2R5V25T4SEZGDRF5C7FNNHWTTF","json":"https://pith.science/pith/2R5V25T4SEZGDRF5C7FNNHWTTF.json","graph_json":"https://pith.science/api/pith-number/2R5V25T4SEZGDRF5C7FNNHWTTF/graph.json","events_json":"https://pith.science/api/pith-number/2R5V25T4SEZGDRF5C7FNNHWTTF/events.json","paper":"https://pith.science/paper/2R5V25T4"},"agent_actions":{"view_html":"https://pith.science/pith/2R5V25T4SEZGDRF5C7FNNHWTTF","download_json":"https://pith.science/pith/2R5V25T4SEZGDRF5C7FNNHWTTF.json","view_paper":"https://pith.science/paper/2R5V25T4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.03594&json=true","fetch_graph":"https://pith.science/api/pith-number/2R5V25T4SEZGDRF5C7FNNHWTTF/graph.json","fetch_events":"https://pith.science/api/pith-number/2R5V25T4SEZGDRF5C7FNNHWTTF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2R5V25T4SEZGDRF5C7FNNHWTTF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2R5V25T4SEZGDRF5C7FNNHWTTF/action/storage_attestation","attest_author":"https://pith.science/pith/2R5V25T4SEZGDRF5C7FNNHWTTF/action/author_attestation","sign_citation":"https://pith.science/pith/2R5V25T4SEZGDRF5C7FNNHWTTF/action/citation_signature","submit_replication":"https://pith.science/pith/2R5V25T4SEZGDRF5C7FNNHWTTF/action/replication_record"}},"created_at":"2026-07-05T01:45:27.345297+00:00","updated_at":"2026-07-05T01:45:27.345297+00:00"}