PerVK: A Robust Personalized Federated Framework to Defend Against Backdoor Attacks for IoT Applications

Robustness and attacks have become prominent concerns in federated learning (FL)-based Internet of Things (IoT). Our focus primarily lies on robustness, as existing robust algorithms are limited by the data distribution and attacker quantity. Personalized FL has emerged as a paradigm to address data heterogeneity, providing personalized local models for participating clients. In this work, we aim to produce personalized models for clients and defend against backdoor attacks on IoT applications by harnessing personalized FL. We propose PerVK, a personalized FL framework that utilizes virtual learning, personalized learning, and knowledge distillation. PerVK effectively reduces data heterogeneity and overcomes the limitations imposed by the number of malicious clients and data distributions. Empirical experiments are conducted on CIFAR-10 and GTSRB datasets, considering various attack scenarios, as well as compared the performance of PerVK with state-of-the-art baselines. The experimental results demonstrate that PerVK successfully defends against backdoor attacks and outperforms existing baselines.