Context-aware multi-user offloading in mobile edge computing: A federated learning-based approach

Mobile edge computing (MEC) provides aneffective solution to help the Internet of Things (IoT)devices with delay-sensitive and computation-intensivetasks by offering computing capabilities in the proximityof mobile device users. Most of the existing studies ignorecontext information of the application, requests, sensors,resources, and network. However, in practice, contextinformation has a significant impact on offloading decisions.In this paper, we consider context-aware offloadingin MEC with multi-user. The contexts are collected usingautonomous management as the MAPE loop in alloffloading processes. Also, federated learning (FL)-basedoffloading is presented. Our learning method in mobiledevices (MDs) is deep reinforcement learning (DRL). FLhelps us to use distributed capabilities of MEC with updatedweights between MDs and edge devices (Eds). Thesimulation results indicate our method is superior to localcomputing, offload, and FL without considering contextawarealgorithms in terms of energy consumption, executioncost, network usage, delay, and fairness