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

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.