Joint Device Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Industrial Internet of Things

计算机科学 Lyapunov优化 带宽分配 最优化问题 杠杆(统计) 能源消耗 数学优化 边缘设备 边缘计算 上传 带宽(计算) 分布式计算 计算机网络 GSM演进的增强数据速率 人工智能 算法 云计算 数学 生态学 混乱的 李雅普诺夫指数 Lyapunov重新设计 操作系统 生物
作者
Xiuzhao Ji,Jie Tian,Haixia Zhang,Dalei Wu,Tiantian Li
出处
期刊:IEEE Internet of Things Journal [Institute of Electrical and Electronics Engineers]
卷期号:10 (10): 9148-9160 被引量:12
标识
DOI:10.1109/jiot.2022.3233595
摘要

Along with the deployment of Industrial Internet of Things (IIoT), massive amounts of industrial data have been generated at the network edge, driving the evolution of edge machine learning (ML). But during the ML model training, it may bring privacy leakage by traditional central methods. To address this issue, federated learning (FL) has been proposed as a distributed learning framework for training a global model without uploading raw data to protect data privacy. Since the communication and computing resources are usually limited in IIoT networks, how to reasonably select device and allocate bandwidth is crucial for the FL model training. Therefore, this article proposes a joint edge device selection and bandwidth allocation scheme for FL to minimize the time-averaged cost under the given long-term energy budget and delay constraints in the IIoT system. To tackle with this long-term optimization problem, we construct a virtual energy deficit queue and leverage the Lyapunov optimization theory to transform it into a list of round-wise drift-plus-cost minimization problems first. Then, we design an iterative algorithm to allocate reasonable bandwidth and select appropriate devices to achieve cost minimization while satisfying the energy consumption constraints. Besides, we develop an optimality analysis of the average cost and energy violation for our proposed scheme. Extensive experiments verify that our proposed scheme can achieve superior performance in cost efficiency over other schemes while guaranteeing FL training performance.
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