Federated-Based Deep Reinforcement Learning (Fed-DRL) for Energy Management in a Distributive Wireless Network

Abstract

Studies on developing future-generation wireless systems are expected to support increased infrastructure development and device subscriptions with densely deployed base stations (BSs). Economically, decreasing BS energy consumption levels and achieving "greenness" remain key factors for the giant industry. Some research works have proposed deep reinforcement techniques to solve energy management (EM) issues in cellular networks. However, these techniques are inefficient in a distributive network environment and expose the devices to privacy issues. Federated learning (FL) is proven to enforce device privacy and train models distributively. Thus, this work proposes an autonomous switching mode framework for BSs based on federated-deep reinforcement learning to address the aforementioned challenges encountered by prior studies. Specifically, we deploy multiple deep reinforcement learning (mDRL) agents to influence the decision of the BS for energy management (EM). On the other hand, to make deep reinforcement learning (DRL)-based decisions feasible and satisfy D-QoS, we train the DRL-agents distributively by employing the federated learning (FL) concept. The results show the effectiveness of our proposed framework under distributed network scenarios compared with other benchmark algorithms.

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