mlsanityData to 14 Oct 2026

Fusion of Federated Learning and Industrial Internet of Things: A Survey

arXiv 2101.00798 ↗First posted 4 Jan 2021cs.NI cs.AIcited by 29

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Abstract (arXiv)

Industrial Internet of Things (IIoT) lays a new paradigm for the concept of Industry 4.0 and paves an insight for new industrial era. Nowadays smart machines and smart factories use machine learning/deep learning based models for incurring intelligence. However, storing and communicating the data to the cloud and end device leads to issues in preserving privacy. In order to address this issue, federated learning (FL) technology is implemented in IIoT by the researchers nowadays to provide safe, accurate, robust and unbiased models. Integrating FL in IIoT ensures that no local sensitive data is exchanged, as the distribution of learning models over the edge devices has become more common with FL. Therefore, only the encrypted notifications and parameters are communicated to the central server. In this paper, we provide a thorough overview on integrating FL with IIoT in terms of privacy, resource and data management. The survey starts by articulating IIoT characteristics and fundamentals of distributive and FL. The motivation behind integrating IIoT and FL for achieving data privacy preservation and on-device learning are summarized. Then we discuss the potential of using machine learning, deep learning and blockchain techniques for FL in secure IIoT. Further we analyze and summarize the ways to handle the heterogeneous and huge data. Comprehensive background on data and resource management are then presented, followed by applications of IIoT with FL in healthcare and automobile industry. Finally, we shed light on challenges, some possible solutions and potential directions for future research.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). Builds on 55 of its 217 references that are in the map. First posted on arXiv 4 Jan 2021.

Builds on

55 of 217 references.

References · 55

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2019Towards Federated Learning at Scale: System Designcited by 832
  5. 2018How To Backdoor Federated Learningcited by 765
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  7. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
  8. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  9. 2019Federated Machine Learning: Concept and Applicationscited by 412
  10. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  11. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  12. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
38 more
  1. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  2. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 228
  3. 2019Improving Adversarial Robustness via Promoting Ensemble Diversitycited by 213
  4. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  5. 2020Threats to Federated Learning: A Surveycited by 178
  6. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 167
  7. 2020Survey of Personalization Techniques for Federated Learningcited by 145
  8. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
  9. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  10. 2018Blockchained On-Device Federated Learningcited by 106
  11. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 100
  12. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  13. 2019Reliable Federated Learning for Mobile Networkscited by 92
  14. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 83
  15. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  16. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  17. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 73
  18. 2020Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approachcited by 56
  19. 2019Benchmarking TPU, GPU, and CPU Platforms for Deep Learningcited by 52
  20. 2018DP-ADMM: ADMM-based Distributed Learning with Differential Privacycited by 42
  21. 2018Multi-objective Evolutionary Federated Learningcited by 42
  22. 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 33
  23. 2020Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networkscited by 30
  24. 2019PuVAE: A Variational Autoencoder to Purify Adversarial Examplescited by 28
  25. 2019Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Datacited by 24
  26. 2020Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surfacecited by 21
  27. 2020Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learningcited by 19
  28. 2019A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Artcited by 18
  29. 2019Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Thingscited by 18
  30. 2020Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Servicescited by 18
  31. 2020Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queriescited by 16
  32. 2019LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learningcited by 10
  33. 2020FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applicationscited by 10
  34. 2018Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approachcited by 9
  35. 2017Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systemscited by 7
  36. 2019The Role of Big Data Analytics in Industrial Internet of Thingscited by 6
  37. 2020Industrial Federated Learning -- Requirements and System Designcited by 6
  38. 2020Intelligent Radio Signal Processing: A Surveycited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 7; 2022: 9; 2023: 4; 2024: 4; 2025: 4; 2026: 1.

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  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  2. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  3. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  4. 2022Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTcited by 25
  5. 2022Artificial Intelligence for the Metaverse: A Surveycited by 25
  6. 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directionscited by 22
  7. 2021Over-the-Air Decentralized Federated Learningcited by 18
  8. 2021Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthinesscited by 15
  9. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  10. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  11. 2022A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehiclescited by 7
  12. 2023Composite federated learning with heterogeneous datacited by 7
17 more
  1. 2021Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learningcited by 6
  2. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  3. 2023Large Scale Foundation Models for Intelligent Manufacturing Applications: A Surveycited by 6
  4. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  5. 2021Homogeneous Learning: Self-Attention Decentralized Deep Learningcited by 4
  6. 2022Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunitiescited by 3
  7. 2022Bayesian data fusion with shared priorscited by 3
  8. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  9. 2024Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustnesscited by 3
  10. 2025Fairness in Federated Learning: Trends, Challenges, and Opportunitiescited by 3
  11. 2022Resource Allocation for Compression-aided Federated Learning with High Distortion Ratecited by 2
  12. 2022Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Thingscited by 2
  13. 2023A survey on deep learning approaches for data integration in autonomous driving systemcited by 1
  14. 2025PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attackscited by 1
  15. 2026Centralized vs Decentralized Federated Learning: A trade-off performance analysiscited by 1
  16. 2024Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoTcited by 0
  17. 2025Distributed Multi-Head Learning Systems for Power Consumption Predictioncited by 0