Enabling Execution Assurance of Federated Learning at Untrusted Participants
Xiaoli Zhang, Fengting Li, Zeyu Zhang, Qi Li, Cong Wang, Jianping Wu
Abstract
Federated learning (FL), as a privacy-preserving machine learning framework, draws growing attention in both industry and academia. It obtains a jointly accurate model by distributing training tasks into data owners and aggregating their model updates. However, FL faces new security problems, as it losses direct control to training processes. One fundamental demand is to ensure whether participants execute training tasks as intended.In this paper, we propose TrustFL, a practical scheme that leverages Trusted Execution Environments (TEEs) to build assurance of participants' training executions with high confidence. Specifically, we use TEE to randomly check a small fraction of all training processes for tunable levels of assurance, while all computations are executed on the co-located faster yet insecure processor (e.g., GPU) for efficiency. To prevent various cheating behaviors like only processing TEE-requested computations or uploading old results, we devise a commitment-based method with specific data selection. We prototype TrustFL using GPU and SGX and evaluate its performance. The results show that TrustFL achieves one/two orders of magnitude speedups compared with naive training with SGX, when assuring correct training with a confidence level of 99%.