Federated deep reinforcement learning-based task offloading system in edge computing environment

Nowadays, Internet of Things (IoT) devices are gaining momentum globally. However, due to their limited size, these devices have limited battery capacity, computational resources, and wireless bandwidth, making it impossible to run resource-intensive applications on these devices. Fortunately, Edge Computing has emerged as a promising solution to meet this demand by enabling data processing in more capable devices. Task offloading is a crucial technique used in Edge Computing to overcome the limitations of IoT devices by offloading some of their computational tasks to more powerful edge servers. The traditional methods used for task offloading are often based on heuristics or simple rules, which may result in sub-optimal solutions. Moreover, the increasing complexity and heterogeneity of edge networks, as well as the stochastic nature of the wireless channel, pose significant challenges for these methods. In this paper, we leverage Federated Learning (FL) to efficiently train Deep Reinforcement Learning (DRL) agents to make the best offloading and power allocation decisions by achieving the near-optimal trade-off between task execution latency and the power consumption of the end device. The obtained simulation results of the proposed method demonstrate its remarkable and superior performance in comparison to central DQN.