FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulation
This addresses the problem of high communication latency for clients in bandwidth-limited wireless federated learning, representing an incremental improvement over existing schemes.
The paper tackles the communication latency issue in wireless federated learning by proposing a layer-wise adaptive modulation scheme, which saves up to 73.9% of communication latency compared to existing methods.
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.