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.

翻译：联邦学习的关键特性在于保护终端用户的数据隐私。然而，在联邦学习框架下交换梯度时仍存在潜在的隐私泄露风险。为此，近期研究常采用差分隐私方法对计算结果添加噪声，以较低开销应对隐私问题，但这会降低模型性能。本文通过利用用户间普遍存在的社交连接，实现了数据隐私与效率的平衡。具体而言，我们提出SCFL——一种新型社交感知的聚类联邦学习方案。在该方案中，相互信任的个体可自由组建社交集群，并在集群内部聚合原始模型更新（如梯度），再将聚合结果上传至云端进行全局聚合。通过混合社交群体内的模型更新，攻击者仅能窃听到社交层组合后的结果，而无法获取个体隐私。SCFL的设计分三步展开：i) 稳定社交集群形成。针对用户异构训练样本与数据分布，我们将最优社交集群形成问题建模为联邦博弈，并设计公平收益分配机制以抵御搭便车行为。ii) 差异化信任-隐私映射。针对低互信集群，我们设计可定制化隐私保护机制，根据社交信任程度自适应净化参与者的模型更新。iii) 分布式收敛。提出分布式双边匹配算法，实现具有纳什稳定收敛性的最优不相交划分。在Facebook网络及MNIST/CIFAR-10数据集上的实验验证表明，SCFL能有效提升学习效用、改善用户收益并实现定制化隐私保护。