Federated Generalized Category Discovery via Personalized Contrastive Graph Learning

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.