As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggregating at the central server side, thereby protecting privacy of users. Mobile users in FL systems typically communicate with base stations (BSs) via wireless channels, where training performance could be degraded due to unreliable access caused by user mobility. However, existing work only investigates a static scenario or random initialization of user locations, which fail to capture mobility in real-world networks. To tackle this issue, we propose a practical model for user mobility in FL across multiple BSs, and develop a user scheduling and resource allocation method to minimize the training delay with constrained communication resources. Specifically, we first formulate an optimization problem with user mobility that jointly considers user selection, BS assignment to users, and bandwidth allocation to minimize the latency in each communication round. This optimization problem turned out to be NP-hard and we proposed a delay-aware greedy search algorithm (DAGSA) to solve it. Simulation results show that the proposed algorithm achieves better performance than the state-of-the-art baselines and a certain level of user mobility could improve training performance.

翻译：作为高效的分布式机器学习方法，联邦学习通过用户侧迭代本地模型训练与中心服务器侧全局模型聚合来获取共享模型，从而保护用户隐私。联邦学习系统中的移动用户通常通过无线信道与基站进行通信，而用户移动性导致的不稳定接入可能降低训练性能。然而，现有工作仅研究静态场景或用户位置的随机初始化，无法捕捉真实网络中的移动性。为解决这一问题，我们提出了一种跨多个基站的联邦学习用户移动性实用模型，并开发了在有限通信资源约束下最小化训练延迟的用户调度与资源配置方法。具体而言，我们首先构建了一个联合考虑用户选择、基站分配与带宽分配以最小化每轮通信延迟的移动性优化问题。该优化问题被证明为NP难问题，我们提出了一种延迟感知贪心搜索算法进行求解。仿真结果表明，所提算法优于当前最优基准方法，且一定程度的用户移动性能提升训练性能。