DeepAI AI Chat
Log In Sign Up

Simultaneous Wireless Information and Power Transfer for Federated Learning

In the Internet of Things, learning is one of most prominent tasks. In this paper, we consider an Internet of Things scenario where federated learning is used with simultaneous transmission of model data and wireless power. We investigate the trade-off between the number of communication rounds and communication round time while harvesting energy to compensate the energy expenditure. We formulate and solve an optimization problem by considering the number of local iterations on devices, the time to transmit-receive the model updates, and to harvest sufficient energy. Numerical results indicate that maximum ratio transmission and zero-forcing beamforming for the optimization of the local iterations on devices substantially boost the test accuracy of the learning task. Moreover, maximum ratio transmission instead of zero-forcing provides the best test accuracy and communication round time trade-off for various energy harvesting percentages. Thus, it is possible to learn a model quickly with few communication rounds without depleting the battery.


page 1

page 2

page 3

page 4


Over-the-Air Federated Learning with Energy Harvesting Devices

We consider federated edge learning (FEEL) among mobile devices that har...

Update Aware Device Scheduling for Federated Learning at the Wireless Edge

We study federated learning (FL) at the wireless edge, where power-limit...

On Federated Learning with Energy Harvesting Clients

Catering to the proliferation of Internet of Things devices and distribu...

Energy Beamforming for Wireless Information and Power Transfer in Backscatter Multiuser Networks

Wirelessly powered backscatter communication (WPBC) has been identified ...

The Magic of Superposition: A Survey on the Simultaneous Transmission Based Wireless Systems

In conventional communication systems, any interference between two comm...

Achieving Model Fairness in Vertical Federated Learning

Vertical federated learning (VFL), which enables multiple enterprises po...

Energy-Harvesting Distributed Machine Learning

This paper provides a first study of utilizing energy harvesting for sus...