{"data":{"id":"92111d56-1272-4ad7-adb2-a4db2891e9e5","title":"Federated Generalized Category Discovery via Personalized Contrastive Graph Learning","summary":"This paper addresses Federated Generalized Category Discovery (Fed-GCD), a task where multiple clients collaborate privately to identify both known and unknown categories in unlabeled data while keeping their data private. The authors propose PCGL (Personalized Contrastive Graph Learning), a framework that separates generic knowledge (shared across all clients) from personalized knowledge (specific to each client) to improve performance for both individual clients and the overall shared model, solving a problem where existing methods create weaker local models by forcing uniform aggregation (combining all client models together) that causes knowledge conflicts.","solution":"N/A -- no mitigation discussed in source.","labels":["research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11674312","publishedAt":"2026-09-01T13:17:12.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-09-01T13:17:12.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.75,"researchCategory":"peer_reviewed","atlasIds":null}}