  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.
