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Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges

arXiv 1908.06847 ↗First posted 30 Jul 2019eess.SP cs.LG stat.MLcited by 128

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Abstract (arXiv)

There is a growing interest in the wireless communications community to complement the traditional model-based design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always feasible in wireless communications applications because of the inaccessibility of private data and large communication overhead required to transmit raw data to central ML processors. As a result, decentralized ML approaches that keep the data where it is generated are much more appealing. Owing to its privacy-preserving nature, federated learning is particularly relevant for many wireless applications, especially in the context of fifth generation (5G) networks. In this article, we provide an accessible introduction to the general idea of federated learning, discuss several possible applications in 5G networks, and describe key technical challenges and open problems for future research on federated learning in the context of wireless communications.

Key facts. Cited by 128 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jul 2019.

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Citing papers per year: 2021: 35; 2022: 19; 2023: 34; 2024: 23; 2025: 10; 2026: 7.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  3. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  4. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  5. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  6. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  7. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  8. 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
  9. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  10. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  11. 2021Federated Learning with Spiking Neural Networkscited by 22
  12. 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directionscited by 22
38 more
  1. 2023Personalized Decentralized Federated Learning with Knowledge Distillationcited by 21
  2. 2024Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysiscited by 21
  3. 2023Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updatescited by 20
  4. 2024PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learningcited by 19
  5. 2021Over-the-Air Decentralized Federated Learningcited by 18
  6. 2023Communication-Efficient Split Learning via Adaptive Feature-Wise Compressioncited by 18
  7. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  8. 2021Communication-Efficient Federated Learning via Quantized Compressed Sensingcited by 17
  9. 2023Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Surveycited by 17
  10. 2021Communication-Efficient Online Federated Learning Framework for Nonlinear Regressioncited by 16
  11. 2021A Systematic Survey of Blockchained Federated Learningcited by 15
  12. 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6Gcited by 14
  13. 2023A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Datacited by 14
  14. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  15. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  16. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  17. 2022Federated learning: Applications, challenges and future directionscited by 11
  18. 2022Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graphcited by 11
  19. 2023Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Applicationcited by 11
  20. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  21. 2022Over-the-Air Federated Learning with Joint Adaptive Computation and Power Controlcited by 8
  22. 2023Expressive variational quantum circuits provide inherent privacy in federated learningcited by 8
  23. 2021Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selectioncited by 7
  24. 2022Towards Energy Efficient Distributed Federated Learning for 6G Networkscited by 7
  25. 2022Federated Learning for Distributed Spectrum Sensing in NextG Communication Networkscited by 7
  26. 2022A Practical Cross-Device Federated Learning Framework over 5G Networkscited by 7
  27. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  28. 2023FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learningcited by 7
  29. 2024Privacy-Preserving in Blockchain-based Federated Learning Systemscited by 7
  30. 2021Personalized Federated Learning over non-IID Data for Indoor Localizationcited by 6
  31. 2021On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestratorcited by 6
  32. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  33. 2022Jamming Attacks on Federated Learning in Wireless Networkscited by 6
  34. 2022Fast-Convergent Federated Learning via Cyclic Aggregationcited by 6
  35. 2022Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networkscited by 6
  36. 2023Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selectioncited by 6
  37. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  38. 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semanticscited by 5

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