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Wireless Communications for Collaborative Federated Learning

arXiv 2006.02499 ↗First posted 3 Jun 2020cs.IT cs.LG math.ITcited by 47

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Abstract (arXiv)

Internet of Things (IoT) services will use machine learning tools to efficiently analyze various types of data collected by IoT devices for inference, autonomy, and control purposes. However, due to resource constraints and privacy challenges, edge IoT devices may not be able to transmit their collected data to a central controller for training machine learning models. To overcome this challenge, federated learning (FL) has been proposed as a means for enabling edge devices to train a shared machine learning model without data exchanges thus reducing communication overhead and preserving data privacy. However, Google's seminal FL algorithm requires all devices to be directly connected with a central controller, which significantly limits its application scenarios. In this context, this paper introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller. The fundamentals of this framework are developed and then, a number of communication techniques are proposed so as to improve the performance of CFL. To this end, an overview of centralized learning, Google's seminal FL, and CFL is first presented. For each type of learning, the basic architecture as well as its advantages, drawbacks, and usage conditions are introduced. Then, three CFL performance metrics are presented and a suite of communication techniques ranging from network formation, device scheduling, mobility management, and coding is introduced to optimize the performance of CFL. For each technique, future research opportunities are also discussed. In a nutshell, this article will showcase how the proposed CFL framework can be effectively implemented at the edge of large-scale wireless systems such as the Internet of Things.

Key facts. Cited by 47 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Jun 2020.

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Citing papers per year: 2021: 12; 2022: 14; 2023: 4; 2024: 8; 2025: 5; 2026: 4.

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  1. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  2. 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
  3. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  4. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  5. 2021Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehiclescited by 26
  6. 2023Federated Generative Learning with Foundation Modelscited by 23
  7. 2021Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunitiescited by 22
  8. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
  9. 2022The Cost of Training Machine Learning Models over Distributed Data Sourcescited by 18
  10. 2023Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devicescited by 13
  11. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  12. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
35 more
  1. 2022Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Pointcited by 10
  2. 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
  3. 2022Non-Coherent Over-the-Air Decentralized Gradient Descentcited by 9
  4. 2022Towards Energy Efficient Distributed Federated Learning for 6G Networkscited by 7
  5. 2022Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relayingcited by 6
  6. 2024R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Modelscited by 5
  7. 2021Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environmentscited by 4
  8. 2022A Comprehensive Survey on Federated Learning: Concept and Applicationscited by 4
  9. 2022Accelerating Federated Edge Learning via Topology Optimizationcited by 4
  10. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  11. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
  12. 2024Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edgecited by 4
  13. 2021Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networkscited by 3
  14. 2025pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Datacited by 3
  15. 2021Federated learning and next generation wireless communications: A survey on bidirectional relationshipcited by 2
  16. 2024On the Federated Learning Framework for Cooperative Perceptioncited by 2
  17. 2024Joint Energy and Latency Optimization in Federated Learning over Cell-Free Massive MIMO Networkscited by 2
  18. 2026Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRAcited by 2
  19. 2022Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applicationscited by 1
  20. 2022Matching Pursuit Based Scheduling for Over-the-Air Federated Learningcited by 1
  21. 2022Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topologycited by 1
  22. 2022AquaFeL-PSO: A Monitoring System for Water Resources using Autonomous Surface Vehicles based on Multimodal PSO and Federated Learningcited by 1
  23. 2023Scheduling and Communication Schemes for Decentralized Federated Learningcited by 1
  24. 2024Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networkscited by 1
  25. 2024Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantizationcited by 1
  26. 2025Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Networkcited by 1
  27. 2026Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysiscited by 1
  28. 2021Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systemscited by 0
  29. 2021Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)cited by 0
  30. 2024Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and futurecited by 0
  31. 2025Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networkscited by 0
  32. 2025Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systemscited by 0
  33. 20256G-Enabled Digital Twin Framework for Real-Time Cyber-Physical Systems: An Experimental Validation with Industrial Bearing Fault Detectioncited by 0
  34. 2026Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuningcited by 0
  35. 2026Federated Hash Projected Latent Factor Learningcited by 0