Federated Learning for Industrial Internet of Things in Future Industries

The Industrial Internet of Things (IIoT) offers promising opportunities to revolutionize the operation of industrial systems and become a key enabler of future industries. Recently, artificial intelligence (AI) has been widely utilized for realizing intelligent IIoT applications where AI techniques require centralized data collection and processing. However, this is not always feasible in realistic scenarios due to the high scalability of modern IIoT networks and growing industrial data confidentiality. Federated Learning (FL), as an emerging collaborative AI approach, is particularly attractive for intelligent IIoT networks by coordinating multiple IIoT devices and machines to perform AI training at the network edge while helping protect user privacy and confidential business information. In this article, we provide a detailed overview and discussions of the emerging applications of FL in several key IIoT services and applications. A case study is also provided to demonstrate the feasibility of FL in IIoT. Finally, we highlight a range of interesting open research topics that need to be addressed for the full realization of FL-IIoT in future industries.

[1]  Shiva Raj Pokhrel,et al.  A Blockchained Federated Learning Framework for Cognitive Computing in Industry 4.0 Networks , 2021, IEEE Transactions on Industrial Informatics.

[2]  Hanseok Ko,et al.  COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network , 2020, IEEE journal of biomedical and health informatics.

[3]  Yang Liu,et al.  Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing , 2019, ArXiv.

[4]  Yifan Yang,et al.  Experiments of Federated Learning for COVID-19 Chest X-ray Images , 2020, Advances in Artificial Intelligence and Security.

[5]  Xiongwen Zhao,et al.  Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT , 2020, IEEE Internet of Things Journal.

[6]  Soumaya Cherkaoui,et al.  Electrical Load Forecasting Using Edge Computing and Federated Learning , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[7]  Yan Zhang,et al.  Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics , 2020, IEEE Transactions on Industrial Informatics.

[8]  Ke Zhang,et al.  Learning Cooperation Schemes for Mobile Edge Computing Empowered Internet of Vehicles , 2020, 2020 IEEE Wireless Communications and Networking Conference (WCNC).

[9]  Zhenyu Zhou,et al.  Learning-Based Queue-Aware Task Offloading and Resource Allocation for Space–Air–Ground-Integrated Power IoT , 2021, IEEE Internet of Things Journal.

[10]  Haomiao Yang,et al.  Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence , 2020, IEEE Transactions on Industrial Informatics.

[11]  Hongwei Li,et al.  Privacy-aware and Resource-saving Collaborative Learning for Healthcare in Cloud Computing , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[12]  Tian Liu,et al.  FDA$^3$: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications , 2020, IEEE Transactions on Industrial Informatics.

[13]  Lu Wang,et al.  Adaptive Federated Learning and Digital Twin for Industrial Internet of Things , 2020, IEEE Transactions on Industrial Informatics.

[14]  Yusheng Ji,et al.  Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues , 2020, IEEE Open Journal of the Computer Society.

[15]  Song Han,et al.  Industrial Internet of Things: Challenges, Opportunities, and Directions , 2018, IEEE Transactions on Industrial Informatics.