Efficient decentralized optimization for edge-enabled smart manufacturing: A federated learning-based framework

Abstract

The volume of industrial data of smart manufacturing is growing rapidly. Edge computing has emerged as an advanced technique that provides scalable resources for Industrial Internet of Things (IIoT) devices to analyze industrial data and enhance productivity. However, real-time and privacy-protecting data services are crucial for comprehensive decision-making, particularly utilizing machine learning in edge-enabled smart manufacturing. Therefore, how to optimize efficiently the data processing has become a critical concern for manufacturers seeking to upgrade the overall manufacturing operations. In this paper, we propose a decentralized federated learning-based framework to ensure the processing of industrial data in a low-latency and secure manner. Our solution designs the combination between edge nodes and IIoT devices as a global consensus problem with equilibrium constraints. Moreover, we adopt the distributed Alternating Direction Method of Multipliers (ADMM) algorithm to optimize the proposed data processing model, considering its advantages of decomposability and parallelism. To flexibly handle various types of industrial data, such as low-rank and high-dimensional data, we present two types of inexact ADMM algorithms to provide efficient model training services, respectively. Furthermore, an integrated optimization flow is designed for data processing in edge-enabled smart manufacturing. Finally, using industrial datasets from a thermal power plant for steam prediction case, we show that the presented inexact algorithms can decrease the response time by up to 17.2% and 58% compared to other existing algorithms, respectively, while achieving the comparable level of statistical accuracy.