RIDE: Robust and Decentralized Federated Learning with Input Validation

Abstract

Federated learning, as an emerging distributed machine learning approach, enables collaborative model training while protecting data privacy. However, federated learning is vulnerable to Byzantine attacks and inference attacks. Existing solutions typically require semi-honest servers to perform secure aggregation or lack effective input validation mechanisms. To address these issues, we propose RIDE, a secure aggregation protocol for decentralized federated learning with input validation. RIDE utilizes pedersen commitments and efficient zero-knowledge proofs to verify whether model updates comply with predefined constraints, ensuring client input privacy and integrity. Additionally, RIDE employs a publicly verifiable secret sharing scheme, ensuring that only validated model updates are aggregated, even in the presence of malicious clients or client dropouts. Experimental results on four real datasets demonstrate the effectiveness of our solution. For example, RIDE has a maximum bandwidth overhead of 7.11MB, which is only 1.31× that of the most popular secure aggregation protocol (CCS 2020), and the computational cost of RIDE’s execution on the CIFAR-10 L dataset is 109.88s, which is 7.28× faster than the current state-of-the-art protocol RoFL (S&P 2023).