TrustNetFL: Enhancing Federated Learning with Trusted Client Aggregation for Improved Security
David Chen, Kehan Wang, Agnideven Palanisamy Sundar, Feng Li · 2023
Federated Learning (FL) has emerged as a promising approach for training machine learning models across individual devices while preserving data privacy. However, FL faces many challenges, specifically a vulnerability to adversarial attacks due to its strict adherence to ensuring individual client model and data privacy. To mitigate these issues, dynamic clipping techniques have been proposed which dynamically adjust the gradient clipping threshold during model aggregation. However, current iterations depend on specific and often intensive calculations to determine a clipping threshold which can lead to an over fitting to a specific dataset or attacker model. In this paper, we focus on improving the limitations of existing FL and dynamic clipping approaches by introducing a novel method that incorporates a group of trusted users during the aggregation of client models for a global update. By identifying and utilizing a network of trusted users, our defense method TrustNetFL enhances the robustness of model aggregation against malicious updates. This method not only maintains the model's performance but also improves its resistance to adversarial influences. We demonstrate the effectiveness of our defense through extensive experiments thus showcasing its superiority and simplicity in achieving enhanced model security in FL settings.