Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation

no code implementations • ICCV 2023 • Guangnian Wan, Haitao Du, Xuejing Yuan, Jun Yang, Meiling Chen, Jie Xu

Previous attacks assume the adversary can infer the local learning rate of each client, while we observe that: (1) using the uniformly distributed random local learning rates does not incur much accuracy loss of the global model, and (2) personalizing local learning rates can mitigate the drift issue which is caused by non-IID (identically and independently distributed) data.

Federated Learning