mlsanityData to 7 Oct 2026

Federated Learning for Internet of Things: A Comprehensive Survey

arXiv 2104.07914 ↗First posted 16 Apr 2021eess.SPcited by 247

Not on the map. Why?

Abstract (arXiv)

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

Key facts. Cited by 247 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2021.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 12; 2022: 34; 2023: 54; 2024: 65; 2025: 51; 2026: 31.

247 papers.

Sort:

By area:

  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  2. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  3. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  4. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 126
  5. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  6. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
  7. 2022DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Trainingcited by 74
  8. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  9. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  10. 2023No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifiercited by 55
  11. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  12. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
38 more
  1. 2023FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policycited by 36
  2. 2021Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Lossescited by 30
  3. 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
  4. 2023GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgettingcited by 27
  5. 2022Private Non-Convex Federated Learning Without a Trusted Servercited by 26
  6. 2022Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matchingcited by 24
  7. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  8. 2023DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learningcited by 23
  9. 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
  10. 2023Federated Learning for Computer Visioncited by 22
  11. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  12. 2024Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modalitycited by 21
  13. 2024Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysiscited by 21
  14. 2023Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updatescited by 20
  15. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  16. 2023Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Surveycited by 17
  17. 2021Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challengescited by 16
  18. 2023A Survey for Federated Learning Evaluations: Goals and Measurescited by 16
  19. 2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devicescited by 16
  20. 2024Text-Enhanced Data-free Approach for Federated Class-Incremental Learningcited by 16
  21. 2025NoT: Federated Unlearning via Weight Negationcited by 15
  22. 2024Federated Distillation: A Surveycited by 14
  23. 2022Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computingcited by 13
  24. 2022Deep Learning Serves Traffic Safety Analysis: A Forward-looking Reviewcited by 13
  25. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  26. 2022Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropoutcited by 12
  27. 2023A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extractioncited by 12
  28. 2023FLGo: A Fully Customizable Federated Learning Platformcited by 12
  29. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  30. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  31. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  32. 2021SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learningcited by 11
  33. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  34. 2024Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approachcited by 11
  35. 2023MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updatescited by 10
  36. 2023Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaborationcited by 10
  37. 2024Poisoning Attacks on Federated Learning-based Wireless Traffic Predictioncited by 10
  38. 2022A Review of Federated Learning in Energy Systemscited by 9

50 most cited of 247 All 247 (JSON)