FedRFQ: Prototype-Based Federated Learning With Reduced Redundancy, Minimal Failure, and Enhanced Quality
Biwei Yan, Hongliang Zhang, Minghui Xu, Dongxiao Yu, Xiuzhen Cheng
Abstract
Federated learning is a powerful technique that enables collaborative learning among different clients. Prototype-based federated learning is a specific approach that improves the performance of local models by integrating class prototypes. However, prototype-based federated learning faces several challenges, such as prototype redundancy and prototype failure, which can limit its accuracy. In addition, it is also susceptible to poisoning attacks and server malfunction, which can degrade the quality of prototypes. To address these issues, we propose FedRFQ, a prototype-based federated learning approach that aims to reduce redundancy, minimize failure, and improve quality. FedRFQ leverages the SoftPool mechanism with prototype-based federated learning, which effectively mitigates prototype redundancy and prototype failure on Non-IID data. Moreover, we introduce the BFT-detect algorithm, a BFT detectable aggregation algorithm, to ensure the security of FedRFQ against poisoning attacks and server malfunction. Finally, we conducted experiments on three different datasets, namely MNIST, FEMNIST, and CIFAR-10. The results demonstrate that FedRFQ outperforms existing baselines in terms of accuracy when handling Non-IID data.