Decentralized Communication-assisted Sensing based on Federated Learning Framework for IIoT.

In Industry 4.0, with the increasing scale of data generated in IIoT, it is necessary for federated learning (FL) algorithms to process and analyze these data in real time, thereby quickly generating high-quality models for edge computing/intelligence. But there are still challenges on current FL frameworks in IIoT, such as difficult client management, prolonged communication delays, and compromised learning effectiveness induced by attacks. To address these challenges, we propose a new FL framework that integrates a digital identity module for user perception and authentication, a decentralized blockchain module for trustworthy FL, and an adaptive model sparsification algorithm for communication-assisted sensing FL. Our FL framework aims to conduct some sense tasks on image classification and sentiment analysis. The effectiveness of our proposed framework for IIoT is demonstrated through technical explanations and experimental results.