Resources-Efficient Adaptive Federated Learning for Digital Twin-Enabled IIoT
Dewen Qiao, Mingyan Li, Songtao Guo, Jun Zhao, Bin Xiao
Abstract
Digital twin (DT) can bridge the physical status with the virtual space in real-time for the Industrial Internet of Things (IIoT), where the integration of federated learning (FL) with DT can enable many edge intelligence services for timely intelligent production in the era of Industry 4.0. However, the issues of heterogeneity of devices and the resource-constrained IIoT make it challenging to achieve efficient FL via DT technology. To handle this problem, we propose a DT-enabled IIoT (DTENI) framework in wireless networks, in which DTs capture the characteristics of industrial devices to enable real-time processing and intelligent decision-making. Specifically, we first analyze the necessity of adaptive wireless parameters (i.e., CPU frequency, bandwidth, and transmission power) on FL training performance and provide theoretical analysis in the DTENI IIoT. Based on the above analysis, we then formulate the minimization problem of FL model loss under a given resource budget, which is a stochastic optimization problem with strongly coupled wireless parameters variables. Benefiting from the model-free learning superiority of deep reinforcement learning (DRL) in dealing with stochastic optimization problems, we develop DTENI-assisted DRL to adaptively adjust the wireless parameters for solving this optimization problem. Lastly, simulation results demonstrate that our proposed scheme can mostly save communication costs up to 74.23%, 69.51%, and 60.94% compared to the three benchmarks.