Social-Aware Clustered Federated Learning with Customized Privacy Preservation

Understand

A key feature of federated learning (FL) is to preserve the data privacy of end users.

• However, there still exist potential privacy leakage in exchanging gradients under FL.
• As a result, recent research often explores the differential privacy (DP) approaches to add noises to the computing results to address privacy concerns with low overheads, which however degrade the model performance.
• In this paper, we strike the balance of data privacy and efficiency by utilizing the pervasive social connections between users.