Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing

no code implementations • 5 Sep 2023 • Zhengrong Song, Chuan Ma, Ming Ding, Howard H. Yang, Yuwen Qian, Xiangwei Zhou

This work proposes a novel solution to address these challenges, namely personalized federated deep reinforcement learning (PF-DRL), for multi-UAV trajectory optimization.

Edge-computing Federated Learning +1

Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation

no code implementations • 15 Aug 2022 • Liang Li, Chenpei Huang, Dian Shi, Hao Wang, Xiangwei Zhou, Minglei Shu, Miao Pan

Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i. e., iterative local computing + multi-round communications) of mobile devices in FL.

Federated Learning

Robotic Communications for 5G and Beyond: Challenges and Research Opportunities

no code implementations • 9 Dec 2020 • Yuanwei Liu, Xiao Liu, Xinyu Gao, Xidong Mu, Xiangwei Zhou, Octavia A. Dobre, H. Vincent Poor

Furthermore, dynamic trajectory design and resource allocation for both indoor and outdoor robots are provided to verify the performance of robotic communications in the context of typical robotic application scenarios.

Robotics Systems and Control Signal Processing Systems and Control