Privacy-preserving and Byzantine-robust federated broad learning with chain-loop structure

Abstract

Federated learning (FL) can collaboratively train a model by aggregating local models instead of aggregating raw data, which can protect privacy by ensuring that data remains on the client. However, the traditional FL still faces some challenges such as privacy leakage and the presence of Byzantine clients. We propose a privacy-preserving and Byzantine-robust federated broad learning framework with chain-loop structure i.e., PBFBL-CL, and this algorithm can simultaneously achieve protection of clients’ privacy and robustness against Byzantine attacks. In this paper, we apply Byzantine step-by-step co-validation algorithm to address the existence of Byzantine clients. We pass the aggregated model through the chain, so each client’s privacy is well protected. Moreover, PBFBL-CL can reduce the communication overhead between clients and server. Finally, we evaluate the PBFBL-CL algorithm in MNIST, Fashion-MNIST and NORB datasets, and the results show that our algorithm is better than existing FL algorithms in terms of model accuracy and training speed. Experimental results demonstrate that under the extreme scenario where Byzantine client proportion reaches 90%, the model achieves an accuracy of 89.53%, only 4.17% lower than the 93.7% accuracy observed in the ideal scenario without Byzantine clients.