  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.
Specifically, we propose SCFL, a novel Social-aware Clustered Federated
Learning scheme, where mutually trusted individuals can freely form a social
cluster and aggregate their raw model updates (e.g., gradients) inside each
cluster before uploading to the cloud for global aggregation. By mixing model
updates in a social group, adversaries can only eavesdrop the social-layer
combined results, but not the privacy of individuals. We unfold the design of
SCFL in three steps.i) Stable social cluster formation. Considering users'
heterogeneous training samples and data distributions, we formulate the optimal
social cluster formation problem as a federation game and devise a fair revenue
allocation mechanism to resist free-riders. ii) Differentiated trust-privacy
mapping}. For the clusters with low mutual trust, we design a customizable
privacy preservation mechanism to adaptively sanitize participants' model
updates depending on social trust degrees. iii) Distributed convergence}. A
distributed two-sided matching algorithm is devised to attain an optimized
disjoint partition with Nash-stable convergence. Experiments on Facebook
network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively
enhance learning utility, improve user payoff, and enforce customizable privacy
protection.
