mlsanityData to 7 Oct 2026

Federated Learning for Internet of Things: Applications, Challenges, and Opportunities

arXiv 2111.07494 ↗First posted 15 Nov 2021cs.LGcited by 49

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Abstract (arXiv)

Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the growth of IoT devices, vast quantities of data that may contain users' private information will be generated. The high communication and storage costs, mixed with privacy concerns, will increasingly challenge the traditional ecosystem of centralized over-the-cloud learning and processing for IoT platforms. Federated Learning (FL) has emerged as the most promising alternative approach to this problem. In FL, training data-driven machine learning models is an act of collaboration between multiple clients without requiring the data to be brought to a central point, hence alleviating communication and storage costs and providing a great degree of user-level privacy. However, there are still some challenges existing in the real FL system implementation on IoT networks. In this paper, we will discuss the opportunities and challenges of FL in IoT platforms, as well as how it can enable diverse IoT applications. In particular, we identify and discuss seven critical challenges of FL in IoT platforms and highlight some recent promising approaches towards addressing them.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). Builds on 21 of its 48 references that are in the map. First posted on arXiv 15 Nov 2021.

Builds on

21 of 48 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  5. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  6. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  7. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  8. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  9. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  10. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
  11. 2018D"IoT: A Federated Self-learning Anomaly Detection System for IoTcited by 89
  12. 2021MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edgecited by 77
9 more
  1. 2020TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learningcited by 32
  2. 2020Experiments of Federated Learning for COVID-19 Chest X-ray Imagescited by 31
  3. 2020Improving on-device speaker verification using federated learning with privacycited by 29
  4. 2020A Federated Learning Framework for Healthcare IoT devicescited by 22
  5. 2021Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detectioncited by 19
  6. 2020Group Knowledge Transfer: Federated Learning of Large CNNs at the Edgecited by 10
  7. 2020Deep Learning in the Era of Edge Computing: Challenges and Opportunitiescited by 10
  8. 2021Secure Aggregation for Buffered Asynchronous Federated Learningcited by 10
  9. 2019Mobility-Aware Smart Charging of Electric Bus Fleetscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 16; 2024: 12; 2025: 11; 2026: 3.

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  1. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  2. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  3. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  4. 2023TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Trainingcited by 27
  5. 2023Federated Domain Generalization: A Surveycited by 25
  6. 2023GPT-FL: Generative Pre-trained Model-Assisted Federated Learningcited by 24
  7. 2023GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learningcited by 18
  8. 2025NoT: Federated Unlearning via Weight Negationcited by 15
  9. 2022Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learningcited by 11
  10. 2023FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategycited by 10
  11. 2023Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaborationcited by 10
  12. 2024Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learningcited by 8
37 more
  1. 2025AugFL: Augmenting Federated Learning with Pretrained Modelscited by 6
  2. 2025You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Datacited by 5
  3. 2024On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representationscited by 4
  4. 2022Towards cost-effective and resource-aware aggregation at Edge for Federated Learningcited by 3
  5. 2023The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directionscited by 3
  6. 2023Learning to Transmit with Provable Guarantees in Wireless Federated Learningcited by 3
  7. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  8. 2024Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Trainingcited by 3
  9. 2024Geminio: Language-Guided Gradient Inversion Attacks in Federated Learningcited by 3
  10. 2023Asynchronous Online Federated Learning with Reduced Communication Requirementscited by 2
  11. 2023Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Reviewcited by 2
  12. 2024Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networkscited by 2
  13. 2022Federated Learning Hyper-Parameter Tuning from a System Perspectivecited by 1
  14. 2023FLSTRA: Federated Learning in Stratospherecited by 1
  15. 2023Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Datacited by 1
  16. 2024Optimal Batch Allocation for Wireless Federated Learningcited by 1
  17. 2024Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learningcited by 1
  18. 2024FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillationcited by 1
  19. 2024Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimizationcited by 1
  20. 2024ModalityMirror: Improving Audio Classification in Modality Heterogeneity Federated Learning with Multimodal Distillationcited by 1
  21. 2024In-depth Analysis of Privacy Threats in Federated Learning for Medical Datacited by 1
  22. 2025Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learningcited by 1
  23. 2025Minimizing Layerwise Activation Norm Improves Generalization in Federated Learningcited by 1
  24. 2022Efficient Adaptive Federated Optimization of Federated Learning for IoTcited by 0
  25. 2022Need of 6G for the Metaverse Realizationcited by 0
  26. 2023Applications of Federated Learning in IoT for Hyper Personalisationcited by 0
  27. 2023A Comprehensive Study on Model Initialization Techniques Ensuring Efficient Federated Learningcited by 0
  28. 2024Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problemscited by 0
  29. 2025Principles and Components of Federated Learning Architecturescited by 0
  30. 2025Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Datacited by 0
  31. 2025EMO: Edge Model Overlays to Scale Model Size in Federated Learningcited by 0
  32. 2025Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preservingcited by 0
  33. 2025Towards a Distributed Federated Learning Aggregation Placement using Particle Swarm Intelligencecited by 0
  34. 2025Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clientscited by 0
  35. 2026Cross-Border Data Security and Privacy Risks in Large Language Models and IoT Systemscited by 0
  36. 2026Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directionscited by 0
  37. 2026FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuningcited by 0