Smart and collaborative industrial IoT: A federated learning and data space approach

作     者：Bahar Farahani Amin Karimi Monsefi Bahar Farahani;Amin Karimi Monsefi

作者机构：Cyberspace Research InstituteShahid Beheshti UniversityTehran***Iran Department of Computer Science and EngineeringOhio State UniversityColumbusOH43210USA 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2023年第9卷第2期

页      面：436-447页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Industry 4.0 Industrial internet of things(IIoT) Artificial intelligence(AI) Predictive maintenance(PdM) Condition monitoring(CM) Federated learning(FL) Privacy preservinig machine learning(PPML) Edge computing Fog computing Cloud computing 

摘      要：Industry 4.0 has become a reality by fusing the Industrial Internet of Things(IIoT)and Artificial Intelligence(AI),providing huge opportunities in the way manufacturing companies ***,the adoption of this paradigm shift,particularly in the field of smart factories and production,is still in its infancy,suffering from various issues,such as the lack of high-quality data,data with high-class imbalance,or poor diversity leading to inaccurate AI ***,data is severely fragmented across different silos owned by several parties for a range of reasons,such as compliance and legal concerns,preventing discovery and insight-driven IIoT ***,valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and *** adversely influences interand intra-organization collaborative use of IIoT *** tackle these challenges,this article leverages emerging multi-party technologies,privacy-enhancing techniques(e.g.,Federated Learning),and AI approaches to present a holistic,decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data ***,to evaluate the efficiency of the proposed reference model,a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT *** results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable,Accessible,Interoperable,and Reusable(FAIR)principles.