按层自适应调制，显著降低无线联邦学习通信延迟。
FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulation
- 根据模型层重要性动态分配调制等级，优化传输效率
- 实测通信延迟最高降低73.9%，优于现有方法
- 适合资源受限的边缘设备部署，提升联邦学习实时性
在无线联邦学习中，客户端需通过带宽受限信道传输高维深度神经网络参数，导致通信延迟问题。本文提出一种分层自适应调制方案，以降低通信延迟。与现有工作对所有网络层采用相同调制等级不同，该方案考虑各层重要性，赋予更多调度自由度，可自动确定各层最优调制水平。实验结果表明，所提方案相较现有方法最多可节省73.9%的通信延迟。
原文摘要 · Abstract (English)
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.
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