"A Privacy-Preserving Federated Learning with Mutual Verification on Ve" by Mingwu Zhang, Chenmei Cui et al.
Federated learning has received widespread attention in recent years, since it trains a model by only sharing gradients without accessing training sets. In this paper, we consider two security issues in the training process of federated learning, i.e., privacy preservation and message verification, which mainly consider the security of the local gradients uploaded by clients and the aggregation result. We give the detail design about the privacy preserving federated learning with mutual authentication, which provides the privacy-preserving and mutually verifiable federated learning framework o...
Source: uow.edu.au