Decentralized Communication-assisted Sensing based on Federated Learning Framework for IIoT
Authors: 
Jian Jin, Zhenzhen Jiao, Junsheng Mu, Wenzhe Lv, Yinan Tang, Tongtong YuanAuthors Info & Claims
Pages 37 - 42
Abstract
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.
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Published In
October 2023
46 pages
ISBN:9798400703645
DOI:10.1145/3615984
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Published: 02 October 2023
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ACM MobiCom '23
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ACM MobiCom '23: The 29th Annual International Conference on Mobile Computing and Networking
October 6, 2023
Madrid, Spain
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