mlsanityData to 14 Oct 2026

Lightweight Federated Learning over Wireless Edge Networks

arXiv 2507.09546 ↗First posted 13 Jul 2025cs.DC cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

With the exponential growth of smart devices connected to wireless networks, data production is increasing rapidly, requiring machine learning (ML) techniques to unlock its value. However, the centralized ML paradigm raises concerns over communication overhead and privacy. Federated learning (FL) offers an alternative at the network edge, but practical deployment in wireless networks remains challenging. This paper proposes a lightweight FL (LTFL) framework integrating wireless transmission power control, model pruning, and gradient quantization. We derive a closed-form expression of the FL convergence gap, considering transmission error, model pruning error, and gradient quantization error. Based on these insights, we formulate an optimization problem to minimize the convergence gap while meeting delay and energy constraints. To solve the non-convex problem efficiently, we derive closed-form solutions for the optimal model pruning ratio and gradient quantization level, and employ Bayesian optimization for transmission power control. Extensive experiments on real-world datasets show that LTFL outperforms state-of-the-art schemes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 13 Jul 2025.

Builds on

14 of 33 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2021Convex optimizationcited by 2,515
  3. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  4. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  5. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  6. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  7. 2020Federated Learning With Quantized Global Model Updatescited by 55
  8. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  9. 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 46
  10. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  11. 2018An Exact Quantized Decentralized Gradient Descent Algorithmcited by 36
  12. 2022Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systemscited by 20
2 more
  1. 2024Generative AI based Secure Wireless Sensing for ISAC Networkscited by 20
  2. 20211-Bit Compressive Sensing for Efficient Federated Learning Over the Aircited by 11

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks
  2. 2022Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
  3. 2024Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
  4. 2024FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
  5. 2025Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
  6. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design
  7. 2024Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks
  8. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks