JSAM: 一种隐私抗延迟的联合客户端选择和激励机制设计在差分隐私联邦学习中
JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning
AI总结：
JSAM通过联合优化客户端选择与隐私补偿，提升差分隐私联邦学习的训练效果和效率，实现隐私保护与参与激励的平衡。
摘要：
差分隐私联邦学习面临一个根本性的矛盾：保护客户端数据的隐私机制同时会产生可衡量的隐私成本，这些成本会抑制参与，从而削弱协作训练过程。现有的激励机制依赖于无偏的客户端选择，迫使服务器补偿甚至最敏感隐私的客户端（
英文摘要：
Differentially private federated learning faces a fundamental tension: privacy protection mechanisms that safeguard client data simultaneously create quantifiable privacy costs that discourage participation, undermining the collaborative training process. Existing incentive mechanisms rely on unbiased client selection, forcing servers to compensate even the most privacy-sensitive clients ("privacy stragglers"), leading to systemic inefficiency and suboptimal resource allocation. We introduce JSAM (Joint client Selection and privacy compensAtion Mechanism), a Bayesian-optimal framework that simultaneously optimizes client selection probabilities and privacy compensation to maximize training effectiveness under budget constraints. Our approach transforms a complex 2N-dimensional optimization problem into an efficient three-dimensional formulation through novel theoretical characterization of optimal selection strategies. We prove that servers should preferentially select privacy-tolerant clients while excluding high-sensitivity participants, and uncover the counter-intuitive insight that clients with minimal privacy sensitivity may incur the highest cumulative costs due to frequent participation. Extensive evaluations on MNIST and CIFAR-10 demonstrate that JSAM achieves up to 15% improvement in test accuracy compared to existing unbiased selection mechanisms while maintaining cost efficiency across varying data heterogeneity levels.
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