mlsanityData to 14 Oct 2026

FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulation

arXiv 2510.07766 ↗First posted 9 Oct 2025cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

In wireless federated learning (FL), the clients need to transmit the high-dimensional deep neural network (DNN) parameters through bandwidth-limited channels, which causes the communication latency issue. In this paper, we propose a layer-wise adaptive modulation scheme to save the communication latency. Unlike existing works which assign the same modulation level for all DNN layers, we consider the layers' importance which provides more freedom to save the latency. The proposed scheme can automatically decide the optimal modulation levels for different DNN layers. Experimental results show that the proposed scheme can save up to 73.9% of communication latency compared with the existing schemes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 9 Oct 2025.

Builds on

18 of 30 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  7. 2019HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precisioncited by 402
  8. 2019PyHessian: Neural Networks Through the Lens of the Hessiancited by 292
  9. 2019HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networkscited by 261
  10. 2020Fast-Convergent Federated Learning with Adaptive Weightingcited by 67
  11. 2020Fast-Convergent Federated Learningcited by 61
  12. 2021Hessian-Aware Pruning and Optimal Neural Implantcited by 45
6 more
  1. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  2. 2020Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noisecited by 21
  3. 2021FedDQ: Communication-Efficient Federated Learning with Descending Quantizationcited by 20
  4. 2022SWIM: Selective Write-Verify for Computing-in-Memory Neural Acceleratorscited by 16
  5. 2024Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Designcited by 3
  6. 2023How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channelscited by 2

Built on by

Not cited yet.