Task offloading mechanism based on federated reinforcement learning in mobile edge computing

With the arrival of 5G, latency-sensitive applications are becoming increasingly diverse. Mobile Edge Computing (MEC) technology has the characteristics of high bandwidth, low latency and low energy consumption, and has attracted much attention among researchers. To improve the Quality of Service (QoS), this study focuses on computation offloading in MEC. We consider the QoS from the perspective of computational cost, dimensional disaster, user privacy and catastrophic forgetting of new users. The QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning (FL) adaptive task offloading algorithm in MEC. The proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing delay. To solve the problems of privacy and catastrophic forgetting, we use FL to make distributed use of multiple users' data to obtain the decision model, protect data privacy and improve the model universality. In the process of FL iteration, the communication delay of individual devices is too large, which affects the overall delay cost. Therefore, we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of FL. The simulation results indicate that compared with existing schemes, the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.