In this paper we propose the federated private local training algorithm (Fed-PLT) for federated learning, to overcome the challenges of (i) expensive communications and (ii) privacy preservation. We address (i) by allowing for both partial participation and local training, which significantly reduce the number of communication rounds between the central coordinator and computing agents. The algorithm matches the state of the art in the sense that the use of local training demonstrably does not impact accuracy. Additionally, agents have the flexibility to choose from various local training solvers, such as (stochastic) gradient descent and accelerated gradient descent. Further, we investigate how employing local training can enhance privacy, addressing point (ii). In particular, we derive differential privacy bounds and highlight their dependence on the number of local training epochs. We assess the effectiveness of the proposed algorithm by comparing it to alternative techniques, considering both theoretical analysis and numerical results from a classification task.

通过提出联邦私有局部训练算法（Fed-PLT），本文解决了联邦学习中昂贵的通信和隐私保护等挑战，通过局部参与和训练，在中央协调员和计算代理之间显著减少通信轮次，实现了局部训练对准确性的无影响匹配。同时，代理具有从不同局部训练求解器中选择的灵活性，如（随机）梯度下降和加速梯度下降，还探讨了如何利用局部训练来增强隐私，并推导了差分隐私边界及其与局部训练轮次的依赖关系，通过理论分析和分类任务的数值结果与其他技术进行比较，评估了所提算法的有效性。

通过本地训练增强联邦学习中的隐私保护