Federated Generalized Category Discovery via Personalized Contrastive Graph Learning

In this work, we investigate the task of Federated Generalized Category Discovery (Fed-GCD), which aims to leverage labeled data from known classes to cluster unlabeled samples from both known and unknown classes through privacy-preserving collaboration among clients. Existing Fed-GCD methods predominantly emphasize improving the performance of the global model, while largely neglecting the personalized performance of local clients—often resulting in local models that perform even worse than those trained solely on local data. Furthermore, existing methods uniformly aggregate these suboptimal local models, which inevitably degrades the global model due to knowledge conflict. To address these limitations, we propose a Personalized Contrastive Graph Learning (PCGL) framework that explicitly decouples generic and personalized knowledge, thereby achieving joint enhancement of both global and local models. Specifically, PCGL comprises two key components: local-graph contrastive learning and personalized knowledge aggregation and extraction. The former introduces a novel KNN-Former that predicts potential contrastive relationships among labeled and unlabeled samples to improve local representation learning. The latter promotes the learning of detachable knowledge via adaptive parameter masking and personalized aggregation, effectively mitigating knowledge conflicts between global and local models. Extensive experiments on six generic and fine-grained datasets demonstrate the superiority of PCGL over state-of-the-art methods, with particularly notable gains on long-tailed natural and medical image datasets by a 4.8% average margin on All ACC.