{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VRJCTBM6HHFOOOQIR32NLGLMAJ","short_pith_number":"pith:VRJCTBM6","schema_version":"1.0","canonical_sha256":"ac5229859e39cae73a088ef4d5996c027a831b7be1adbbb8fa96390d05c6baa6","source":{"kind":"arxiv","id":"2602.10765","version":2},"attestation_state":"computed","paper":{"title":"Collaborative Threshold Watermarking","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anish Ambreth, Nils Lukas, Tameem Bakr","submitted_at":"2026-02-11T11:51:41Z","abstract_excerpt":"In federated learning (FL), $K$ clients jointly train a model without sharing raw data. Because each participant invests data and compute, clients need mechanisms to later prove the provenance of a jointly trained model. Model watermarking embeds a hidden signal in the weights, but naive approaches either do not scale with many clients as per-client watermarks dilute as $K$ grows, or give any individual client the ability to verify and potentially remove the watermark. We introduce $(t,K)$-threshold watermarking: clients collaboratively embed a shared watermark during training, while only coal"},"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":"2602.10765","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-11T11:51:41Z","cross_cats_sorted":[],"title_canon_sha256":"627e5ade29fb526fa7fd7589e9371703bc74e1f31f76007f2264de044be2839f","abstract_canon_sha256":"78eae4e66123061d5059cf636f35f94438e71ca569b1eb102b319aa27d8cc568"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:04:36.915935Z","signature_b64":"OQMh3937PtyQsTSgTOZEFkGAQs8hzMz98t9HKERT1Dm/jkDytpESudZ8ndzEiFLuTynaYq+/XWc5lHrvRiCKBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac5229859e39cae73a088ef4d5996c027a831b7be1adbbb8fa96390d05c6baa6","last_reissued_at":"2026-05-29T01:04:36.915409Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:04:36.915409Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Collaborative Threshold Watermarking","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anish Ambreth, Nils Lukas, Tameem Bakr","submitted_at":"2026-02-11T11:51:41Z","abstract_excerpt":"In federated learning (FL), $K$ clients jointly train a model without sharing raw data. Because each participant invests data and compute, clients need mechanisms to later prove the provenance of a jointly trained model. Model watermarking embeds a hidden signal in the weights, but naive approaches either do not scale with many clients as per-client watermarks dilute as $K$ grows, or give any individual client the ability to verify and potentially remove the watermark. We introduce $(t,K)$-threshold watermarking: clients collaboratively embed a shared watermark during training, while only coal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.10765","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/2602.10765/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":"2602.10765","created_at":"2026-05-29T01:04:36.915474+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.10765v2","created_at":"2026-05-29T01:04:36.915474+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.10765","created_at":"2026-05-29T01:04:36.915474+00:00"},{"alias_kind":"pith_short_12","alias_value":"VRJCTBM6HHFO","created_at":"2026-05-29T01:04:36.915474+00:00"},{"alias_kind":"pith_short_16","alias_value":"VRJCTBM6HHFOOOQI","created_at":"2026-05-29T01:04:36.915474+00:00"},{"alias_kind":"pith_short_8","alias_value":"VRJCTBM6","created_at":"2026-05-29T01:04:36.915474+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/VRJCTBM6HHFOOOQIR32NLGLMAJ","json":"https://pith.science/pith/VRJCTBM6HHFOOOQIR32NLGLMAJ.json","graph_json":"https://pith.science/api/pith-number/VRJCTBM6HHFOOOQIR32NLGLMAJ/graph.json","events_json":"https://pith.science/api/pith-number/VRJCTBM6HHFOOOQIR32NLGLMAJ/events.json","paper":"https://pith.science/paper/VRJCTBM6"},"agent_actions":{"view_html":"https://pith.science/pith/VRJCTBM6HHFOOOQIR32NLGLMAJ","download_json":"https://pith.science/pith/VRJCTBM6HHFOOOQIR32NLGLMAJ.json","view_paper":"https://pith.science/paper/VRJCTBM6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.10765&json=true","fetch_graph":"https://pith.science/api/pith-number/VRJCTBM6HHFOOOQIR32NLGLMAJ/graph.json","fetch_events":"https://pith.science/api/pith-number/VRJCTBM6HHFOOOQIR32NLGLMAJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VRJCTBM6HHFOOOQIR32NLGLMAJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VRJCTBM6HHFOOOQIR32NLGLMAJ/action/storage_attestation","attest_author":"https://pith.science/pith/VRJCTBM6HHFOOOQIR32NLGLMAJ/action/author_attestation","sign_citation":"https://pith.science/pith/VRJCTBM6HHFOOOQIR32NLGLMAJ/action/citation_signature","submit_replication":"https://pith.science/pith/VRJCTBM6HHFOOOQIR32NLGLMAJ/action/replication_record"}},"created_at":"2026-05-29T01:04:36.915474+00:00","updated_at":"2026-05-29T01:04:36.915474+00:00"}