12 citations · 23 across the 5 of their papers we have counts for
4 papers · 1 filter
WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling
Huai-an Su, Jiaxiang Geng, Liang Li +5
As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating…
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation
Liang Li, Chenpei Huang, Dian Shi +4
Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to pr…
Towards Energy Efficient Federated Learning over 5G+ Mobile Devices
Dian Shi, Liang Li, Rui Chen +3
The continuous convergence of machine learning algorithms, 5G and beyond (5G+) wireless communications, and artificial intelligence (AI) hardware implementation hastens the birth o…
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices
Liang Li, Dian Shi, Ronghui Hou +3
Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which ope…