P-EVFL: Efficient verifiable federated learning with privacy

Abstract

Federated learning has recently become popular and widely used in various areas. However, it still faces challenges like the leakage of the client’s local model updates and the server forging aggregation results. To address these issues, we propose an efficient verifiable federated learning scheme with privacy (P-EVFL), which seeks to ensure privacy and verifiability with a lower overhead. Specifically, we first design a lightweight masking technique to protect the honest clients’ local model updates. Next, we introduce homomorphic hash functions to develop a verifiable method to ensure the integrity of the aggregation results. Besides, to reduce the overhead of the verification process, a verification algorithm based on a Merkle tree is proposed. We also conduct comprehensive experiments and compare our scheme with other state-of-the-art schemes. The experimental results show that in a scenario with 100 clients, our scheme reduces the computational overhead by up to 8.15 % and the communication overhead by up to 67.38 %.