Modeling on Resource Allocation for Age-Sensitive Mobile-Edge Computing Using Federated Multiagent Reinforcement Learning.

Cong Wang , Tianye Yao, Tingshan Fan,Sancheng Peng,Changming Xu,Shui Yu

IEEE Internet of Things Journal(2024)

引用 1|浏览16
暂无评分
摘要
Existing mobile-edge computing (MEC) systems are facing the challenges of limited resources and highly dynamic network environments. How to allocate resources to maintain the efficiency and timeliness of data and tasks is still an open issue. To address this problem, we propose a novel framework for unmanned aerial vehicle-assisted MEC systems using federated multiagent reinforcement learning (RL). First, we formulate a joint optimization problem as a multiagent Markov decision process by jointly minimizing the average age of information and maximizing the number of recent tasks. Second, we design a novel scheduling algorithm for online collaborative resources by adopting multiple agents to learn and make decisions in accordance with the overall interests through federal learning. Finally, an experience replay mechanism for the internal experience pool is introduced to further improve learning efficiency. Experimental results show that our proposed algorithm is superior to the recent typical RL-based algorithms. It not only has higher efficiency in task processing and data freshness but also has more stable performance and adaptability across diverse experimental conditions.
更多
查看译文
关键词
Federated learning (FL),mobile-edge computing (MEC),multiagent deep reinforcement learning (MARL),resource allocation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要