社交感知的聚类联邦学习与定制化隐私保护
Social-Aware Clustered Federated Learning with Customized Privacy Preservation
AI总结：
针对联邦学习中的隐私泄露与性能权衡问题，提出社交感知聚类联邦学习SCFL，利用用户社交信任形成聚类内部聚合以隐藏个体更新，并通过博弈论与匹配算法实现稳定聚类和定制化隐私保护，实验验证其提升效用与收益。
摘要：
联邦学习（FL）的一个关键特性是保护终端用户的数据隐私。然而，在联邦学习下交换梯度时仍存在潜在的隐私泄露。因此，近期研究常采用差分隐私（DP）方法，向计算结果添加噪声以低开销解决隐私问题，但这会降低模型性能。在本文中，我们利用用户之间普遍存在的社交联系，来平衡数据隐私与效率。具体而言，我们提出了SCFL，一种新颖的社交感知聚类联邦学习方案，其中相互信任的个体可以自由形成社交聚类，并在每个聚类内部聚合其原始模型更新（如梯度），然后再上传至云端进行全局聚合。通过在社交群体中混合模型更新， adversaries 只能窃听到社交层合并后的结果，而无法获取个体的隐私。我们分三步展开SCFL的设计。i) 稳定的社交聚类形成。考虑到用户异构的训练样本和数据分布，我们将最优社交聚类形成问题建模为联邦博弈，并设计了一种公平的收益分配机制以抵御搭便车者。ii) 差异化的信任-隐私映射。对于互信度较低的聚类，我们设计了一种可定制的隐私保护机制，根据社交信任度自适应地净化参与者的模型更新。iii) 分布式收敛。我们设计了一种分布式双边匹配算法，以获得具有纳什稳定收敛的最优不相交划分。在Facebook网络和MNIST/CIFAR-10数据集上的实验验证了我们的SCFL能有效提升学习效用、提高用户收益，并实施定制化的隐私保护。
英文摘要：
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.
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