Client Selection and Resource Allocation for Federated Learning in Digital-Twin-Enabled Industrial Internet of Things

Abstract

The technological advancements in digital twins (DTs) and next-generation networks boost the development of the Industrial Internet of Things (IIoT). However, the increasing concern for data privacy hinders the further development of IIoT. By manipulating its own dataset and training machine learning models locally, federated learning (FL) has been regarded as a promising technology to establish DT models for IIoT. Nevertheless, the vast amount of model parameters, limited computation and communication resources, and the dynamic industrial environments pose great challenges for FL. In this paper, we present a novel FL framework integrating with DTs in the resource-constrained and dynamic IIoT environment. The clustering algorithm is exploited to design an efficient client selection approach. Furthermore, we propose an efficient model aggregation approach to improve the training efficiency. A resource allocation problem is then formulated and solved based on deep reinforcement learning. Numerical results show that the proposed client selection and resource allocation approach reduces the time cost compared with the baseline schemes.