Federated Learning With Energy Harvesting Devices: An MDP Framework

• URL: http://arxiv.org/abs/2405.10513v1
• Date: Fri, 17 May 2024 03:41:40 GMT
• Title: Federated Learning With Energy Harvesting Devices: An MDP Framework
• Authors: Kai Zhang, Xuanyu Cao,
• Abstract summary: Federated learning (FL) requires edge devices to perform local training and exchange information with a parameter server. A critical challenge in practical FL systems is the rapid energy depletion of battery-limited edge devices. We apply energy harvesting technique in FL systems to extract ambient energy for continuously powering edge devices.
• Score: 5.852486435612777
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) requires edge devices to perform local training and exchange information with a parameter server, leading to substantial energy consumption. A critical challenge in practical FL systems is the rapid energy depletion of battery-limited edge devices, which curtails their operational lifespan and affects the learning performance. To address this issue, we apply energy harvesting technique in FL systems to extract ambient energy for continuously powering edge devices. We first establish the convergence bound for the wireless FL system with energy harvesting devices, illustrating that the convergence is impacted by partial device participation and packet drops, both of which depend on the energy supply. To accelerate the convergence, we formulate a joint device scheduling and power control problem and model it as a Markov decision process (MDP). By solving this MDP, we derive the optimal transmission policy and demonstrate that it possesses a monotone structure with respect to the battery and channel states. To overcome the curse of dimensionality caused by the exponential complexity of computing the optimal policy, we propose a low-complexity algorithm, which is asymptotically optimal as the number of devices increases. Furthermore, for unknown channels and harvested energy statistics, we develop a structure-enhanced deep reinforcement learning algorithm that leverages the monotone structure of the optimal policy to improve the training performance. Finally, extensive numerical experiments on real-world datasets are presented to validate the theoretical results and corroborate the effectiveness of the proposed algorithms.

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