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：联邦学习的核心特性之一是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，通过向计算结果中添加噪声来以较低开销应对隐私问题，但该方法会降低模型性能。本文利用用户间普遍存在的社交连接来平衡数据隐私与效率。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。该方案中，互信个体可自由组建社交簇，在簇内聚合原始模型更新（如梯度）后上传至云端进行全局聚合。通过社交群组内的模型更新混合，攻击者仅能窃听到社交层面的组合结果，而无法获取个体隐私。我们从三个层面展开SCFL设计：①稳定社交簇构建。针对用户异构训练样本与数据分布，将最优簇构建问题建模为联邦博弈，并设计公平收益分配机制抑制搭便车行为。②差异化信任-隐私映射。针对低互信度的簇，设计可定制的隐私保护机制，依据社交信任度自适应净化参与者模型更新。③分布式收敛。提出分布式双边匹配算法，在纳什稳定收敛条件下实现最优不相交簇划分。在Facebook网络及MNIST/CIFAR-10数据集上的实验表明，SCFL能有效提升学习效用、改善用户收益，并实现可定制的隐私保护。