mlsanityData to 14 Oct 2026

Federated Learning for Industrial Internet of Things in Future Industries

arXiv 2105.14659 ↗First posted 31 May 2021cs.LG eess.SPcited by 14

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Abstract (arXiv)

The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for 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. In this article, we provide a detailed overview and discussions of the emerging applications of FL in 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 industries.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 5 of its 25 references that are in the map. First posted on arXiv 31 May 2021.

Builds on

5 of 25 references.

References · 5

  1. 2020Experiments of Federated Learning for COVID-19 Chest X-ray Imagescited by 31
  2. 2020COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Networkcited by 29
  3. 2020Adaptive Federated Learning and Digital Twin for Industrial Internet of Thingscited by 27
  4. 2019Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensingcited by 11
  5. 2020FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applicationscited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 5; 2025: 4; 2026: 1.

14 papers.

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  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  2. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  3. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  4. 2024Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challengescited by 14
  5. 2021Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networkscited by 6
  6. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  7. 2024Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sourcescited by 4
  8. 2024Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustnesscited by 3
  9. 2025Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MRcited by 3
  10. 2024Federated Progressive Self-Distillation with Logits Calibration for Personalized IIoT Edge Intelligencecited by 1
  11. 2024Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoTcited by 0
  12. 2025Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Gamecited by 0
2 more
  1. 2025Learning to Collaborate Over Graphs: A Selective Federated Multi-Task Learning Approachcited by 0
  2. 2026Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networkscited by 0