ERD-FL: Entropy-Driven Robust Defense for Federated Learning
Okba Ben Atia, Mustafa Al Samara, Ismail Bennis, Jaafar Gaber, Abdelhafid Abouaïssa, Pascal Lorenz · 2024
Federated Learning (FL) is a crucial technology in decentralized Machine Learning (ML), prominently used within Internet of Things (IoT) networks to enhance data privacy. However, it is threatened by poisoning attacks, where harmful data alterations can significantly disrupt learning processes. This paper introduces a novel solution, Entropy-based Robust Defense Federated Learning (ERDFL), to counteract these disruptions. Our approach leverages entropy information for enhanced detection of malicious models and also innovatively adjusts detection thresholds in real-time, thereby effectively identifying and excluding potentially malicious clients within the FL process. Our simulation results, using the Mnist, Fashion-Mnist, and IMDB datasets, demonstrate that our novel approach surpasses other previously studied approaches in the literature across multiple performance metrics, including Accuracy Rate (ACC), Attack Success Rate(ASR), Loss Rate (LR) and CPU aggregation run-time.