{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:CR4PR6U2EYUDFY4QWU54SU5BH7","short_pith_number":"pith:CR4PR6U2","schema_version":"1.0","canonical_sha256":"1478f8fa9a262832e390b53bc953a13ff0d8eafd23801aa4cff35907523be9fa","source":{"kind":"arxiv","id":"1803.09737","version":2},"attestation_state":"computed","paper":{"title":"DJAM: distributed Jacobi asynchronous method for learning personal models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","stat.ML"],"primary_cat":"cs.LG","authors_text":"In\\^es Almeida, Jo\\~ao Xavier","submitted_at":"2018-03-26T17:53:56Z","abstract_excerpt":"Processing data collected by a network of agents often boils down to solving an optimization problem. The distributed nature of these problems calls for methods that are, themselves, distributed. While most collaborative learning problems require agents to reach a common (or consensus) model, there are situations in which the consensus solution may not be optimal. For instance, agents may want to reach a compromise between agreeing with their neighbors and minimizing a personal loss function. We present DJAM, a Jacobi-like distributed algorithm for learning personalized models. This method is "},"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":"1803.09737","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-26T17:53:56Z","cross_cats_sorted":["cs.DC","stat.ML"],"title_canon_sha256":"d0ea11af27d931460af045a181716c7918624d695d3e5be488f8d84e24161bdb","abstract_canon_sha256":"46684e7055756899a9757e8ec8efdb5059227c9e6c1645cb054266609ffc49e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:08.138561Z","signature_b64":"QPOl0NPkSW/D94IClCGvqCtNSMY4OfH2vYHQaaLcpt6iQxnXNNfW6X058g1IuQ/TbSYt3lZ0aSrQHi7dCZ0ADA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1478f8fa9a262832e390b53bc953a13ff0d8eafd23801aa4cff35907523be9fa","last_reissued_at":"2026-05-18T00:07:08.137780Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:08.137780Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DJAM: distributed Jacobi asynchronous method for learning personal models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","stat.ML"],"primary_cat":"cs.LG","authors_text":"In\\^es Almeida, Jo\\~ao Xavier","submitted_at":"2018-03-26T17:53:56Z","abstract_excerpt":"Processing data collected by a network of agents often boils down to solving an optimization problem. The distributed nature of these problems calls for methods that are, themselves, distributed. While most collaborative learning problems require agents to reach a common (or consensus) model, there are situations in which the consensus solution may not be optimal. For instance, agents may want to reach a compromise between agreeing with their neighbors and minimizing a personal loss function. We present DJAM, a Jacobi-like distributed algorithm for learning personalized models. This method is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.09737","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1803.09737","created_at":"2026-05-18T00:07:08.137918+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.09737v2","created_at":"2026-05-18T00:07:08.137918+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.09737","created_at":"2026-05-18T00:07:08.137918+00:00"},{"alias_kind":"pith_short_12","alias_value":"CR4PR6U2EYUD","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"CR4PR6U2EYUDFY4Q","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"CR4PR6U2","created_at":"2026-05-18T12:32:16.446611+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CR4PR6U2EYUDFY4QWU54SU5BH7","json":"https://pith.science/pith/CR4PR6U2EYUDFY4QWU54SU5BH7.json","graph_json":"https://pith.science/api/pith-number/CR4PR6U2EYUDFY4QWU54SU5BH7/graph.json","events_json":"https://pith.science/api/pith-number/CR4PR6U2EYUDFY4QWU54SU5BH7/events.json","paper":"https://pith.science/paper/CR4PR6U2"},"agent_actions":{"view_html":"https://pith.science/pith/CR4PR6U2EYUDFY4QWU54SU5BH7","download_json":"https://pith.science/pith/CR4PR6U2EYUDFY4QWU54SU5BH7.json","view_paper":"https://pith.science/paper/CR4PR6U2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.09737&json=true","fetch_graph":"https://pith.science/api/pith-number/CR4PR6U2EYUDFY4QWU54SU5BH7/graph.json","fetch_events":"https://pith.science/api/pith-number/CR4PR6U2EYUDFY4QWU54SU5BH7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CR4PR6U2EYUDFY4QWU54SU5BH7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CR4PR6U2EYUDFY4QWU54SU5BH7/action/storage_attestation","attest_author":"https://pith.science/pith/CR4PR6U2EYUDFY4QWU54SU5BH7/action/author_attestation","sign_citation":"https://pith.science/pith/CR4PR6U2EYUDFY4QWU54SU5BH7/action/citation_signature","submit_replication":"https://pith.science/pith/CR4PR6U2EYUDFY4QWU54SU5BH7/action/replication_record"}},"created_at":"2026-05-18T00:07:08.137918+00:00","updated_at":"2026-05-18T00:07:08.137918+00:00"}