FVFL: A Flexible and Verifiable Privacy-Preserving Federated Learning Scheme

With the development of deep learning, people are more and more concerned about the security of data. Federated learning can solve the problem of data island, but it also brings more serious data privacy problems. Furthermore, in the process of multisource data collaboration, the efficiency of the whole federated learning system is usually not high. In this article, we introduce a scheme named FVFL, which ensure the local data security and resistance to collusive attacks, more importantly it can well support client flexible participate federated learning. We adopt Paillier encryption and secret sharing to guarantee client's data security and resistance to collusive attacks. Moreover, our encryption mechanism allows client to participate in federated learning flexibly, and the correctness of the encryption algorithm is not affected by client's drop out. The super-increasing sequence is introduced to reduce the communication overhead of the whole system, the simulation result shows that the result is significant; the Lagrange interpolation polynomial and secret Sharing is introduced to implement verification mechanism, to prevent malicious forgery of aggregation results in the cloud. The verification mechanism ensures the clients to obtain real and reliable aggregation results in the cloud. Moreover, our verification mechanism allows client to participate in federated learning flexibly, and the correctness of the verification algorithm is not affected by client's drop out. And the experimental results show that FVFL has high accuracy and efficiency.