This paper proposes a locally differentially private federated learning algorithm for strongly convex but possibly nonsmooth problems that protects the gradients of each worker against an honest but curious server. The proposed algorithm adds artificial noise to the shared information to ensure privacy and dynamically allocates the time-varying noise variance to minimize an upper bound of the optimization error subject to a predefined privacy budget constraint. This allows for an arbitrarily large but finite number of iterations to achieve both privacy protection and utility up to a neighborhood of the optimal solution, removing the need for tuning the number of iterations. Numerical results show the superiority of the proposed algorithm over state-of-the-art methods.

翻译：本文提出了一种针对强凸但可能非光滑问题的局部差分隐私联邦学习算法，该算法能够保护每个工作节点在应对诚实但好奇的服务器时的梯度信息。所提算法向共享信息中添加人工噪声以确保隐私，并动态分配时变噪声方差，以在预定义隐私预算约束下最小化优化误差的上界。这使得算法能够在实现隐私保护的同时，达到最优解邻域内的效用，且迭代次数可任意大但有限，从而无需调整迭代次数。数值结果表明，所提算法优于现有最先进方法。