Social-Aware Clustered Federated Learning with Customized Privacy Preservation. (arXiv:2212.13992v1 [cs.CR])

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. …

aware federated learning preservation privacy social