Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks

• URL: http://arxiv.org/abs/2104.11320v1
• Date: Fri, 2 Apr 2021 18:41:59 GMT
• Title: Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks
• Authors: Sheyda Zarandi and Hina Tabassum
• Abstract summary: We propose a federated deep reinforcement learning framework to solve a multi-objective optimization problem. To enhance the learning speed of IoT devices (agents), we incorporate federated learning (FDL) at the end of each episode. Our numerical results demonstrate the efficacy of our proposed federated DDQN framework in terms of learning speed.
• Score: 12.599009485247283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a federated deep reinforcement learning framework to solve a multi-objective optimization problem, where we consider minimizing the expected long-term task completion delay and energy consumption of IoT devices. This is done by optimizing offloading decisions, computation resource allocation, and transmit power allocation. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we first cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using double deep Q-network (DDQN), where the actions are offloading decisions. The immediate cost of each agent is calculated through solving either the transmit power optimization or local computation resource optimization, based on the selected offloading decisions (actions). Then, to enhance the learning speed of IoT devices (agents), we incorporate federated learning (FDL) at the end of each episode. FDL enhances the scalability of the proposed DRL framework, creates a context for cooperation between agents, and minimizes their privacy concerns. Our numerical results demonstrate the efficacy of our proposed federated DDQN framework in terms of learning speed compared to federated deep Q network (DQN) and non-federated DDQN algorithms. In addition, we investigate the impact of batch size, network layers, DDQN target network update frequency on the learning speed of the FDL.

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