Enabling Privacy-Preserving and Publicly Auditable Federated Learning

arXiv:2405.04029v1 Announce Type: new
Abstract: Federated learning (FL) has attracted widespread attention because it supports the joint training of models by multiple participants without moving private dataset. However, there are still many security issues in FL that deserve discussion. In this paper, we consider three major issues: 1) how to ensure that the training process can be publicly audited by any third party; 2) how to avoid the influence of malicious participants on training; 3) how to ensure that private …

arxiv attention cs.cr dataset federated federated learning major moving privacy private security security issues training