RPPFL: Robust and Privacy-Preserving Federated Learning via Trusted Execution Environments

Abstract

Federated Learning (FL) is a distributed framework that enables multi-participant collaborative model training without the need for data sharing. Despite its advantages, FL is vulnerable to poisoning and inference attacks, which compromise model accuracy and data privacy. Trusted execution environments (TEEs) offer a potential solution by providing a secure and isolated execution space to address these security and privacy concerns in FL. However, existing TEE-based FL schemes often suffer from reduced training speed and compromised model accuracy. To mitigate these issues, we propose a robust and privacy-preserving framework for federated learning (RPPFL) that leverages TEE and pseudorandom masking. In our approach, a trusted local model is trained on a secure subset of local data within the client-side TEE, which is then used for anomaly detection to resist poisoning attacks. Additionally, we employ pseudorandom masking to obfuscate local updates and global parameters. Experimental results indicate that RPPFL effectively counters both poisoning and inference attacks, with only a minimal decrease in training speed and no adverse impact on model accuracy. Compared to full-TEE approaches, our method improved local training efficiency by 10× , with less than a 9% loss in model performance under poisoning attacks.