计算机科学
异步通信
边缘计算
异步学习
GSM演进的增强数据速率
分布式计算
适应性学习
边缘设备
多媒体
人机交互
人工智能
同步学习
计算机网络
操作系统
云计算
数学教育
数学
教学方法
合作学习
作者
Wenhao Liu,Taiping Cui,Bin Shen,Xiaoge Huang,Qianbin Chen
标识
DOI:10.1109/wcsp58612.2023.10404949
摘要
This paper introduces an innovative solution to enhance transportation in smart traffic systems by combining federated learning with edge computing. However, traditional synchronous federated learning lacks efficiency for real-time requirements, while asynchronous federated learning consumes more energy and exhibits unstable training. To address these challenges, the paper proposes an Adaptive Waiting Time Asynchronous Federated Learning (AWTAFL) algorithm based on the Dueling Double Deep Q-Network (D3QN). This algorithm dynamically adjusts the waiting time using D3QN, considering task progress and energy consumption, to accelerate convergence and save energy. Furthermore, the paper improves global model aggregation in federated learning by incorporating data volume weights, freshness level of client parameters, and client contribution level. This comprehensive parameter weighing ensures stability during asynchronous federated learning and aids in achieving convergence. Experimental simulations confirm that the proposed algorithm significantly reduces convergence time, maintains model quality, and effectively reduces energy consumption during asynchronous federated learning. Moreover, the improved global aggregation update method enhances training stability and reduces oscillations in the global model convergence.
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