mlsanityData to 14 Oct 2026

Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design

arXiv 2407.13703 ↗First posted 26 Jun 2024cs.IT cs.LG eess.SPcited by 3

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Abstract (arXiv)

One of the most critical challenges for deploying distributed learning solutions, such as federated learning (FL), in wireless networks is the limited battery capacity of mobile clients. While it is a common belief that the major energy consumption of mobile clients comes from the uplink data transmission, this paper presents a novel finding, namely channel decoding also contributes significantly to the overall energy consumption of mobile clients in FL. Motivated by this new observation, we propose an energy-efficient adaptive channel decoding scheme that leverages the intrinsic robustness of FL to model errors. In particular, the robustness is exploited to reduce the energy consumption of channel decoders at mobile clients by adaptively adjusting the number of decoding iterations. We theoretically prove that wireless FL with communication errors can converge at the same rate as the case with error-free communication provided the bit error rate (BER) is properly constrained. An adaptive channel decoding scheme is then proposed to improve the energy efficiency of wireless FL systems. Experimental results demonstrate that the proposed method maintains the same learning accuracy while reducing the channel decoding energy consumption by ~20% when compared to an existing approach.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 29 of its 56 references that are in the map. First posted on arXiv 26 Jun 2024.

Builds on

29 of 56 references.

References · 29

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  8. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 193
  9. 2019Federated Learning over Wireless Fading Channelscited by 183
  10. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  11. 2015Adding Gradient Noise Improves Learning for Very Deep Networkscited by 145
  12. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 81
17 more
  1. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 73
  2. 2021Adaptive Quantization of Model Updates for Communication-Efficient Federated Learningcited by 63
  3. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  4. 2022ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsitycited by 45
  5. 2021AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learningcited by 39
  6. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  7. 2019Robust Federated Learning with Noisy Communicationcited by 34
  8. 2020Convergence of Federated Learning over a Noisy Downlinkcited by 29
  9. 2022FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergencecited by 29
  10. 2023Green Edge AI: A Contemporary Surveycited by 25
  11. 2020Device Heterogeneity in Federated Learning: A Superquantile Approachcited by 21
  12. 2021FedDQ: Communication-Efficient Federated Learning with Descending Quantizationcited by 20
  13. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Designcited by 15
  14. 2023Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learningcited by 9
  15. 2021Federated Learning over Noisy Channels: Convergence Analysis and Design Examplescited by 8
  16. 2022Broadband Digital Over-the-Air Computation for Wireless Federated Edge Learningcited by 6
  17. 2023How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channelscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Noise-Robust and Resource-Efficient ADMM-based Federated Learningcited by 1
  2. 2025FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulationcited by 0
  3. 2026Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learningcited by 0