Federated Generalized Novel Category Discovery with Prompts Tuning

Generalized category discovery (GCD) is proposed to handle categories from unseen labels during the inference stage by clustering them. Most works in GCD provide solutions for unseen classes in data-centralized settings. However, unlabeled categories possessed by clients, which are common in real-world federated learning (FL), have been largely ignored and degraded the performance of classic FL algorithms. To demonstrate and mitigate the harmful effect of unseen classes, we dive into a GCD problem setting applicable for FL named FedGCD, analyze overfitting problem in FedGCD in detail, establish a strong baseline constructed with state-of-the-art GCD algorithm simGCD, and design a learning framework with prompt tuning to tackle both the overfitting and communication burden problems in FedGCD. In our methods, clients first separately carry out prompt learning on local data. Then, we aggregate the prompts from all clients as the global prompt to help capture global knowledge and then send the global prompts to local clients to allow access to broader knowledge from other clients. By this method, we significantly reduce the parameters needed to upload in FedGCD, which is a common obstacle in the real application of most FL algorithms. We conduct experiments on both generic and fine-grained datasets like CIFAR-100 and CUB-200, and show that our method is comparable to the FL version of simGCD and surpasses other baselines with significantly fewer parameters to transmit.