Node Selection Algorithm for Federated Learning Based on Deep Reinforcement Learning for Edge Computing in IoT
Shuai Yan, Peiying Zhang, Siyu Huang, Jian Wang, Hao Sun, Yi Zhang, Amr Tolba
Abstract
The Internet of Things (IoT) and edge computing technologies have been rapidly developing in recent years, leading to the emergence of new challenges in privacy and security. Personal privacy and data leakage have become major concerns in IoT edge computing environments. Federated learning has been proposed as a solution to address these privacy issues, but the heterogeneity of devices in IoT edge computing environments poses a significant challenge to the implementation of federated learning. To overcome this challenge, this paper proposes a novel node selection strategy based on deep reinforcement learning to optimize federated learning in heterogeneous device IoT environments. Additionally, a metric model for IoT devices is proposed to evaluate the performance of different devices. The experimental results demonstrate that the proposed method can improve training accuracy by 30% in a heterogeneous device IoT environment.