FedTA: Unsupervised Federated Prototype Learning with Temperature Adaptation

Federated Learning (FL) has emerged as a foundational paradigm that enables collaborative training of deep neural networks across distributed clients while ensuring data privacy. However, most of the existing research primarily concentrates on supervised federated learning tailored for specific downstream tasks. In this paper, we concentrate on unsupervised federated learning, where clients collaborate to identify data patterns, structures, or representations. Contrastive learning methods have proven highly effective for unsupervised learning in server-based environments. However, a straightforward adaptation of the contrastive learning approach for this setting falls short. Upon analyzing client behaviors during training, we identified an unstable training process stemming from NonIID issues, which can result in diminished performance. To address this problem, we propose a novel Temperature-Adaption based unsupervised Federated prototype learning approach, termed FedTA. This method incorporates two straightforward yet potent solutions: 1. Prototype Enhancement and 2. Temperature-Adaption. Experimental results show that our proposed approach surpasses the state-of-the-art, achieving a performance accuracy improvement of up to 5.3%.