CRACAU: Byzantine Machine Learning Meets Industrial Edge Computing in Industry 5.0
Anran Du, Yi‐Cheng Shen, Qinzi Zhang, Lewis Tseng, Moayad Aloqaily
Abstract
Industry 5.0 is emerging as a result of the advancement in networking and communication technologies, artificial intelligence, distributed computing, and beyond 5G. Among the important enabling technologies, federated learning, industrial edge computing, and Byzantine-tolerant machine learning (ML) are key accelerators in Industry 5.0. We propose a framework to integrate these key components. Recent works have designed various Byzantine-tolerant ML algorithms for a datacenter or a cluster. However, these algorithms are difficult to be applied to industrial edge computing paradigms. In this article, a novel Byzantine-tolerant federated learning algorithm, CRACAU, is designed for the popular three-level edge computing architecture. In this algorithm, edge devices jointly learn an ML model using the data collected at each device, and their private data are never shared with others. Under standard assumptions, we formally prove that CRACAU converges to the optimal point, i.e., CRACAU finds the optimal parameters of the ML model. We also implement CRACAU in the MXNet framework and evaluate it on the popular benchmark MNIST and CIFAR-10 image classification datasets. Experimental results show that CRACAU achieves satisfying accuracy.