Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy. (arXiv:2202.07178v2 [cs.LG] UPDATED)

Federated learning (FL) that enables edge devices to collaboratively learn a
shared model while keeping their training data locally has received great
attention recently and can protect privacy in comparison with the traditional
centralized learning paradigm. However, sensitive information about the
training data can still be inferred from model parameters shared in FL.
Differential privacy (DP) is the state-of-the-art technique to defend against
those attacks. The key challenge to achieving DP in FL lies in the adverse
impact of DP …

client differential privacy federated learning privacy under