Personalized Federated Deep Reinforcement Learning-based Trajectory
  Optimization for Multi-UAV Assisted Edge Computing
Zhengrong Song Chuan Ma Ming Ding Howard H. Yang Yuwen Qian Xiangwei Zhou
In the era of 5G mobile communication, there has been a significant surge in
research focused on unmanned aerial vehicles (UAVs) and mobile edge computing
technology. UAVs can serve as intelligent servers in edge computing
environments, optimizing their flight trajectories to maximize communication
system throughput. Deep reinforcement learning (DRL)-based trajectory
optimization algorithms may suffer from poor training performance due to
intricate terrain features and inadequate training data. To overcome this
limitation, some studies have proposed leveraging federated learning (FL) to
mitigate the data isolation problem and expedite convergence. Nevertheless, the
efficacy of global FL models can be negatively impacted by the high
heterogeneity of local data, which could potentially impede the training
process and even compromise the performance of local agents. This work proposes
a novel solution to address these challenges, namely personalized federated
deep reinforcement learning (PF-DRL), for multi-UAV trajectory optimization.
PF-DRL aims to develop individualized models for each agent to address the data
scarcity issue and mitigate the negative impact of data heterogeneity.
Simulation results demonstrate that the proposed algorithm achieves superior
training performance with faster convergence rates, and improves service
quality compared to other DRL-based approaches.