{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5NOQWOWZZXPQQJKIHLWTKESLES","short_pith_number":"pith:5NOQWOWZ","schema_version":"1.0","canonical_sha256":"eb5d0b3ad9cddf0825483aed35124b24b2b1740e2a0cbe75ef782b447b532c56","source":{"kind":"arxiv","id":"2303.04906","version":2},"attestation_state":"computed","paper":{"title":"Model-Agnostic Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Gianluca Mittone, Iacopo Colonnelli, Marco Aldinucci, Robert Birke, Walter Riviera","submitted_at":"2023-03-08T21:51:14Z","abstract_excerpt":"Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grad"},"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":"2303.04906","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-08T21:51:14Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"41c2b322b543210695cabfda0924dcaae23788e3c9de8a898d6cc033283e6bbb","abstract_canon_sha256":"87dd9a13667d2006c991d116f825b0dac3190e14daaefb8599713eb14b032bf4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:59.521956Z","signature_b64":"rapPv1Z//VP7PnqQEGDVHnNz0heT681F1fdNSTECS5GNoXe5W9REHxvCpWhnC4WTCSXyASlVBOqcjcqOmjCKAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb5d0b3ad9cddf0825483aed35124b24b2b1740e2a0cbe75ef782b447b532c56","last_reissued_at":"2026-07-05T07:01:59.521461Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:59.521461Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Model-Agnostic Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Gianluca Mittone, Iacopo Colonnelli, Marco Aldinucci, Robert Birke, Walter Riviera","submitted_at":"2023-03-08T21:51:14Z","abstract_excerpt":"Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grad"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.04906","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.04906/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":"2303.04906","created_at":"2026-07-05T07:01:59.521520+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.04906v2","created_at":"2026-07-05T07:01:59.521520+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.04906","created_at":"2026-07-05T07:01:59.521520+00:00"},{"alias_kind":"pith_short_12","alias_value":"5NOQWOWZZXPQ","created_at":"2026-07-05T07:01:59.521520+00:00"},{"alias_kind":"pith_short_16","alias_value":"5NOQWOWZZXPQQJKI","created_at":"2026-07-05T07:01:59.521520+00:00"},{"alias_kind":"pith_short_8","alias_value":"5NOQWOWZ","created_at":"2026-07-05T07:01:59.521520+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/5NOQWOWZZXPQQJKIHLWTKESLES","json":"https://pith.science/pith/5NOQWOWZZXPQQJKIHLWTKESLES.json","graph_json":"https://pith.science/api/pith-number/5NOQWOWZZXPQQJKIHLWTKESLES/graph.json","events_json":"https://pith.science/api/pith-number/5NOQWOWZZXPQQJKIHLWTKESLES/events.json","paper":"https://pith.science/paper/5NOQWOWZ"},"agent_actions":{"view_html":"https://pith.science/pith/5NOQWOWZZXPQQJKIHLWTKESLES","download_json":"https://pith.science/pith/5NOQWOWZZXPQQJKIHLWTKESLES.json","view_paper":"https://pith.science/paper/5NOQWOWZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.04906&json=true","fetch_graph":"https://pith.science/api/pith-number/5NOQWOWZZXPQQJKIHLWTKESLES/graph.json","fetch_events":"https://pith.science/api/pith-number/5NOQWOWZZXPQQJKIHLWTKESLES/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5NOQWOWZZXPQQJKIHLWTKESLES/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5NOQWOWZZXPQQJKIHLWTKESLES/action/storage_attestation","attest_author":"https://pith.science/pith/5NOQWOWZZXPQQJKIHLWTKESLES/action/author_attestation","sign_citation":"https://pith.science/pith/5NOQWOWZZXPQQJKIHLWTKESLES/action/citation_signature","submit_replication":"https://pith.science/pith/5NOQWOWZZXPQQJKIHLWTKESLES/action/replication_record"}},"created_at":"2026-07-05T07:01:59.521520+00:00","updated_at":"2026-07-05T07:01:59.521520+00:00"}