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 under non-IID (non-Independently and Identically Distributed) settings by integrating class prototypes. However, prototype-based federated learning faces several challenges, such as prototype redundancy and prototype failure, which limit its accuracy. It is also susceptible to poisoning attacks and server malfunctions, which can degrade the prototype quality. To address these issues, we propose FedRFQ, a prototype-based federated learning approach that aims to reduce redundancy, minimize failures, and improve \underline{q}uality. FedRFQ leverages a SoftPool mechanism, which effectively mitigates prototype redundancy and prototype failure on non-IID data. Furthermore, we introduce the BFT-detect, a BFT (Byzantine Fault Tolerance) detectable aggregation algorithm, to ensure the security of FedRFQ against poisoning attacks and server malfunctions. Finally, we conduct experiments on three different datasets, namely MNIST, FEMNIST, and CIFAR-10, and the results demonstrate that FedRFQ outperforms existing baselines in terms of accuracy when handling non-IID data.

翻译：联邦学习是一种强大的技术，能够实现不同客户端之间的协作学习。基于原型的联邦学习是一种特定方法，通过整合类原型来提升非独立同分布（non-IID）设置下本地模型的性能。然而，基于原型的联邦学习面临若干挑战，例如原型冗余和原型失效，这些问题限制了其准确性。此外，该方法易受投毒攻击和服务器故障的影响，从而降低原型质量。为解决这些问题，我们提出FedRFQ，一种旨在降低冗余、最小化故障并提升质量的原型联邦学习方法。FedRFQ利用SoftPool机制，有效缓解非IID数据上的原型冗余和原型失效问题。同时，我们引入BFT-detect——一种可检测拜占庭故障（BFT）的聚合算法，以保障FedRFQ免受投毒攻击和服务器故障的影响。最后，我们在三个不同数据集（MNIST、FEMNIST和CIFAR-10）上进行了实验，结果表明，在处理非IID数据时，FedRFQ在准确性上优于现有基线方法。