Defending Byzantine Failures for Federated Learning

Ang Li, Ke Xing, Yanjie Dong

Abstract: With the rapid expansion of Internet of Things (IoT) applications, federated learning (FL) has emerged as a critical technology for managing distributed datasets. However, Byzantine faults pose significant security threats to the practical deployment of FL in IoT contexts. This research aims to evaluate the impact of these threats on FL implementations in IoT environments. Specifically, we conducted experiments within the Federated Stochastic Gradient Descent (FedSGD) framework using the CIFAR-10 dataset to evaluate the resilience of FL against various Byzantine attacks. These experiments involved testing seven types of Byzantine attacks and assessing the accuracy and effectiveness of nine different Byzantine defense mechanisms. We introduced a novel performance metric, $P_{BD1}$, which enhanced our ability to comprehensively evaluate the effectiveness of these defenses. Our results indicate that while most defense mechanisms exhibit varying degrees of effectiveness, DnC (Divide-and-Conquer) and ClippedClustering emerged as the most promising techniques. Additionally, the new performance metric PBD1 has proven to be a valuable tool for comprehensive evaluation.