Federated Learning (FL) is a distributed machine learning technique designed to utilize the
distributed datasets collected by our mobile and internet-of-things devices. As such, it is
natural to consider wireless communication for FL. In wireless networks, Over-the-Air
Computation (AirComp) can accelerate FL training by harnessing the interference of uplink
gradient transmissions. However, since AirComp utilizes analog transmissions, it introduces
an inevitable estimation error due to channel fading and noise. In this paper, we propose …

Motivated by increasing computational capabilities of wireless devices, as well as
unprecedented levels of user-and device-generated data, new distributed machine learning
(ML) methods have emerged. In the wireless community, Federated Learning (FL) is of
particular interest due to its communication efficiency and its ability to deal with the problem
of non-IID data. FL training can be accelerated by a wireless communication method called
Over-the-Air Computation (AirComp) which harnesses the interference of simultaneous …