mlsanityData to 7 Oct 2026

Distributed Learning in Wireless Networks: Recent Progress and Future Challenges

arXiv 2104.02151 ↗First posted 5 Apr 2021cs.LG cs.IT math.ITcited by 125

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Abstract (arXiv)

The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment, limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources. This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks. We present a detailed overview of several emerging distributed learning paradigms, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. For each learning framework, we first introduce the motivation for deploying it over wireless networks. Then, we present a detailed literature review on the use of communication techniques for its efficient deployment. We then introduce an illustrative example to show how to optimize wireless networks to improve its performance. Finally, we introduce future research opportunities. In a nutshell, this paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks.

Key facts. Cited by 125 papers in the map (data through 7 Oct 2026). Builds on 147 of its 211 references that are in the map. First posted on arXiv 5 Apr 2021.

Builds on

147 of 211 references.

References · 147

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50 most cited of 147 All 147 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 32; 2023: 25; 2024: 27; 2025: 20; 2026: 13.

125 papers.

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  1. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
  2. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  3. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
  4. 2024Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designscited by 32
  5. 2022Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AIcited by 31
  6. 2023Green Edge AI: A Contemporary Surveycited by 24
  7. 2022A Survey on Over-the-Air Computationcited by 19
  8. 2022The Cost of Training Machine Learning Models over Distributed Data Sourcescited by 18
  9. 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
  10. 2023Federated Quantum Long Short-term Memory (FedQLSTM)cited by 15
  11. 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
  12. 2022Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verificationcited by 14
38 more
  1. 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Surveycited by 14
  2. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  3. 2024Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approachcited by 12
  4. 2022Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learningcited by 11
  5. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  6. 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
  7. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  8. 2024CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Networkcited by 10
  9. 2024DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Modelscited by 10
  10. 2025Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Frameworkcited by 10
  11. 2021Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Schedulingcited by 9
  12. 2022Over-the-Air Ensemble Inference with Model Privacycited by 9
  13. 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learningcited by 9
  14. 2022Non-Coherent Over-the-Air Decentralized Gradient Descentcited by 9
  15. 2022MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehiclescited by 8
  16. 2023Optimization of Image Transmission in a Cooperative Semantic Communication Networkscited by 8
  17. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  18. 2022Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learningcited by 7
  19. 2022Federated Learning for Distributed Spectrum Sensing in NextG Communication Networkscited by 7
  20. 2023Federated Learning for Water Consumption Forecasting in Smart Citiescited by 7
  21. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  22. 2021How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learningcited by 6
  23. 2022Jamming Attacks on Federated Learning in Wireless Networkscited by 6
  24. 2022Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networkscited by 6
  25. 2024Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approachescited by 6
  26. 2024Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networkscited by 6
  27. 2025The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networkscited by 6
  28. 2022Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relayingcited by 5
  29. 2022Rate-Constrained Remote Contextual Banditscited by 5
  30. 2022In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networkscited by 5
  31. 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semanticscited by 5
  32. 2023Efficient Multiuser AI Downloading via Reusable Knowledge Broadcastingcited by 5
  33. 2023Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approachcited by 5
  34. 2024Decentralized Federated Learning Over Imperfect Communication Channelscited by 5
  35. 2024R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Modelscited by 5
  36. 2022Over-the-Air Computation over Balanced Numeralscited by 4
  37. 2023Secure Federated Learning for Cognitive Radio Sensingcited by 4
  38. 2023Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AIcited by 4

50 most cited of 125 All 125 (JSON)