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.

联邦学习是一种高效的分布式机器学习方法，通过用户端的迭代本地模型训练和中央服务器端的全局模型聚合，保护用户的隐私。然而，现有的工作只研究了静态场景或用户位置的随机初始化，未能捕捉到真实网络中的用户移动。为了解决这个问题，在联邦学习中引入了用户的移动模型，并开发了一种用户调度和资源分配方法，以减小由于通信资源有限而带来的训练延迟。模拟结果表明，所提出的算法相比最先进的基线算法具有更好的性能，一定程度的用户移动可以提高训练性能。

针对低延迟联邦学习的移动感知联合用户调度和资源分配

Mobility-Aware Joint User Scheduling and Resource Allocation for Low  Latency Federated Learning

Mobile edge learning is an emerging technique that enables distributed edge
devices to collaborate in training shared machine learning models by exploiting
their local data samples and communication and computation resources. To deal
with the straggler dilemma issue faced in this technique, this paper proposes a
new device to device enabled data sharing approach, in which different edge
devices share their data samples among each other over communication links, in
order to properly adjust their computation loads for increasing the training
speed. Under this setup, we optimize the radio resource allocation for both
data sharing and distributed training, with the objective of minimizing the
total training delay under fixed numbers of local and global iterations.
Numerical results show that the proposed data sharing design significantly
reduces the training delay, and also enhances the training accuracy when the
data samples are non independent and identically distributed among edge
devices.

本文提出使用不同边缘设备间通过通信链接共享数据样本的新设备到设备的数据共享方法，在此设置下，优化了无线电资源分配以减少训练时延，并在数据样本在边缘设备之间非独立且非同分布时增加了训练精度。