{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:35FBPKALZFZQR6ONT62U5UOXMO","short_pith_number":"pith:35FBPKAL","schema_version":"1.0","canonical_sha256":"df4a17a80bc97308f9cd9fb54ed1d763afe00ed07d11d3bdd7ee9b8aaf074fb7","source":{"kind":"arxiv","id":"2507.12903","version":1},"attestation_state":"computed","paper":{"title":"Federated Learning for Commercial Image Sources","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Koteswar Rao Jerripothula, Shreyansh Jain","submitted_at":"2025-07-17T08:42:48Z","abstract_excerpt":"Federated Learning is a collaborative machine learning paradigm that enables multiple clients to learn a global model without exposing their data to each other. Consequently, it provides a secure learning platform with privacy-preserving capabilities. This paper introduces a new dataset containing 23,326 images collected from eight different commercial sources and classified into 31 categories, similar to the Office-31 dataset. To the best of our knowledge, this is the first image classification dataset specifically designed for Federated Learning. We also propose two new Federated Learning al"},"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":"2507.12903","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-17T08:42:48Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"24e101b477a1d3d53f98a67d7680230161b864ee7e40c6f26c350c4fc2cc90cc","abstract_canon_sha256":"6c993961217222adfea9ea294d458b8b69d0bb48847dff5120b4c44dbaac54dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:38:41.162557Z","signature_b64":"1lskyO1HRPWDqMh4nfnden6EFf3EG3VlgueFag9Fbvx5z0WCfM6jipA1qfWMEthaYFTf+gX5hqaFhrSU3/lMBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df4a17a80bc97308f9cd9fb54ed1d763afe00ed07d11d3bdd7ee9b8aaf074fb7","last_reissued_at":"2026-07-05T11:38:41.162001Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:38:41.162001Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Learning for Commercial Image Sources","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Koteswar Rao Jerripothula, Shreyansh Jain","submitted_at":"2025-07-17T08:42:48Z","abstract_excerpt":"Federated Learning is a collaborative machine learning paradigm that enables multiple clients to learn a global model without exposing their data to each other. Consequently, it provides a secure learning platform with privacy-preserving capabilities. This paper introduces a new dataset containing 23,326 images collected from eight different commercial sources and classified into 31 categories, similar to the Office-31 dataset. To the best of our knowledge, this is the first image classification dataset specifically designed for Federated Learning. We also propose two new Federated Learning al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.12903","kind":"arxiv","version":1},"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/2507.12903/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":"2507.12903","created_at":"2026-07-05T11:38:41.162059+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.12903v1","created_at":"2026-07-05T11:38:41.162059+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.12903","created_at":"2026-07-05T11:38:41.162059+00:00"},{"alias_kind":"pith_short_12","alias_value":"35FBPKALZFZQ","created_at":"2026-07-05T11:38:41.162059+00:00"},{"alias_kind":"pith_short_16","alias_value":"35FBPKALZFZQR6ON","created_at":"2026-07-05T11:38:41.162059+00:00"},{"alias_kind":"pith_short_8","alias_value":"35FBPKAL","created_at":"2026-07-05T11:38:41.162059+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/35FBPKALZFZQR6ONT62U5UOXMO","json":"https://pith.science/pith/35FBPKALZFZQR6ONT62U5UOXMO.json","graph_json":"https://pith.science/api/pith-number/35FBPKALZFZQR6ONT62U5UOXMO/graph.json","events_json":"https://pith.science/api/pith-number/35FBPKALZFZQR6ONT62U5UOXMO/events.json","paper":"https://pith.science/paper/35FBPKAL"},"agent_actions":{"view_html":"https://pith.science/pith/35FBPKALZFZQR6ONT62U5UOXMO","download_json":"https://pith.science/pith/35FBPKALZFZQR6ONT62U5UOXMO.json","view_paper":"https://pith.science/paper/35FBPKAL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.12903&json=true","fetch_graph":"https://pith.science/api/pith-number/35FBPKALZFZQR6ONT62U5UOXMO/graph.json","fetch_events":"https://pith.science/api/pith-number/35FBPKALZFZQR6ONT62U5UOXMO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/35FBPKALZFZQR6ONT62U5UOXMO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/35FBPKALZFZQR6ONT62U5UOXMO/action/storage_attestation","attest_author":"https://pith.science/pith/35FBPKALZFZQR6ONT62U5UOXMO/action/author_attestation","sign_citation":"https://pith.science/pith/35FBPKALZFZQR6ONT62U5UOXMO/action/citation_signature","submit_replication":"https://pith.science/pith/35FBPKALZFZQR6ONT62U5UOXMO/action/replication_record"}},"created_at":"2026-07-05T11:38:41.162059+00:00","updated_at":"2026-07-05T11:38:41.162059+00:00"}