Mobility-Aware Joint User Scheduling and Resource Allocation for Low
  Latency Federated Learning
Kecheng Fan Wen Chen Jun Li Xiumei Deng Xuefeng Han Ming Ding
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.