12 citations · 27 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
Service Delay Minimization for Federated Learning over Mobile Devices
Rui Chen, Dian Shi, Xiaoqi Qin +3
Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay eff…
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…