Task Offloading and Resource Allocation for Fog Computing in NG Wireless Networks: A Federated Deep Reinforcement Learning Approach
Chan Su, Jianguo Wei, Deyu Lin, Linghe Kong, Yong Liang Guan
Abstract
Task offloading (TO) is beneficial to reducing the delay and energy consumption for the prosperity of the applications in next generation (NG) wireless networks. However, existing TO approaches are inability to exhibit low complexity and stable performance. To this end, a novel federated hierarchical deep deterministic policy gradient (FHDDPG) algorithm for TO and resource allocation (RA) is proposed in this article. To be specific, three deep deterministic policy gradient (DDPG) modules are deployed in parallel to make offloading decision on the execution mode of tasks and the proportion allocation of the transmission rate. Subsequently, a federated learning method is proposed to collaboratively train the HDDPG model by means of sharing models’ weights. Meanwhile, the delay and the energy consumption are comprehensively considered as the average system consumption, which is defined as a reward metric of FHDDPG. Finally, extensive simulations are conducted to demonstrate the effectiveness of our proposal. The experimental results indicate that the average system consumption of FHDDPG is cut down by 11.4% and 18% compare with HDDPG and DDPG, respectively, which means FHDDPG can achieve a better performance effectively.