3 papers
cs.LG2025
FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulation
Linping Qu, Shenghui Song, Chi-Ying Tsui
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 com…
cs.IT2024
Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design
Linping Qu, Yuyi Mao, Shenghui Song +1
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 cl…
cs.LG2024
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
Linping Qu, Shenghui Song, Chi-Ying Tsui
Federated learning (FL) is a powerful machine learning paradigm which leverages the data as well as the computational resources of clients, while protecting clients' data privacy.…