A Byzantine-Robust Federated Learning Algorithm Based on Matrix Mapping

Federated learning can better protect data privacy because the parameter server only collects the client model and does not touch the local data of the client. However, its basic aggregation algorithm FedAvg is vulnerable to Byzantine client attacks. In response to this problem, many studies have proposed different aggregation algorithms, but these aggregation algorithms have insufficient defensive capabilities, and the model assumptions do not fit the reality. Therefore, we propose a new type of Byzantine robust aggregation algorithm. Different from the existing aggregation algorithms, our algorithm focuses on detecting the probability distribution of the Softmax layer. Specifically, after collecting the client model, the parameter server obtains the Softmax layer probability distribution of the model through the generated matrix to map the updated part of the model, and eliminates the client model with abnormal distribution. The experimental results show that without reducing the accuracy of FedAvg, the Byzantine tolerance rate is increased from 40% to 45% in convergence prevention attacks, and the defense against edge-case backdoor attacks is realized in backdoor attacks. In addition, according to the current state-of-the-art adaptive attack framework, an adaptive attack is designed specifically for our algorithm, and experimental evaluations have been carried out. The experimental results show that our aggregation algorithm can defend at least 30% of Byzantine clients.