SVFL: A secure and verifiable federated learning scheme

Abstract

Federated learning can effectively alleviate the data privacy problem of the participants, but the parameters or gradients passed in the model training may still leak the private data of the participants. Worse, aggregation server may return fake aggregation results. Existing solutions either use complex cryptographic primitives such as zero-knowledge proofs, or require interaction among participants, causing them high computation or communication overhead. Therefore, this paper proposes a secure and verifiable federated learning (SVFL) scheme. Specifically, SVFL performs privacy protection by introducing noise that can be offset during the aggregation process, and utilizes linear homomorphic hash to verify the correctness of the aggregation results. Compared with existing schemes, SVFL hardly loses accuracy due to the introduced security mechanism, and has low computation and communication overhead. Experimental results show that the performance of SVFL is almost consistent with the original federated learning without any protection, which makes SVFL applicable to edge devices. The computation and communication overhead of SVFL does not increase with the number of participants, which makes SVFL applicable to high-concurrency scenarios.