Search Results for author:
Found 4 papers, 0 papers with code
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation
Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i. e., iterative local computing + multi-round communications) of mobile devices in FL.
Service Delay Minimization for Federated Learning over Mobile Devices
In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices.
Towards Energy Efficient Federated Learning over 5G+ Mobile Devices
The continuous convergence of machine learning algorithms, 5G and beyond (5G+) wireless communications, and artificial intelligence (AI) hardware implementation hastens the birth of federated learning (FL) over 5G+ mobile devices, which pushes AI functions to mobile devices and initiates a new era of on-device AI applications.
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices
Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications.
