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

arXiv: https://arxiv.org/abs/2510.07766 · HTML: https://mlsanity.com/paper/2510.07766/

## 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 (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision](https://mlsanity.com/paper/1905.03696/) (2019)
- [PyHessian: Neural Networks Through the Lens of the Hessian](https://mlsanity.com/paper/1912.07145/) (2019)
- [HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks](https://mlsanity.com/paper/1911.03852/) (2019)
- [Fast-Convergent Federated Learning with Adaptive Weighting](https://mlsanity.com/paper/2012.00661/) (2020)
- [Fast-Convergent Federated Learning](https://mlsanity.com/paper/2007.13137/) (2020)
- [Hessian-Aware Pruning and Optimal Neural Implant](https://mlsanity.com/paper/2101.08940/) (2021)
- [Quantized Federated Learning under Transmission Delay and Outage Constraints](https://mlsanity.com/paper/2106.09397/) (2021)
- [Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise](https://mlsanity.com/paper/2007.09208/) (2020)
- [FedDQ: Communication-Efficient Federated Learning with Descending Quantization](https://mlsanity.com/paper/2110.02291/) (2021)
- [SWIM: Selective Write-Verify for Computing-in-Memory Neural Accelerators](https://mlsanity.com/paper/2202.08395/) (2022)
- [Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design](https://mlsanity.com/paper/2407.13703/) (2024)
- [How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels](https://mlsanity.com/paper/2310.16652/) (2023)

## 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.