Over-the-air federated learning (OTA-FL) is a distributed machine learning technique where
multiple devices collaboratively train a shared model without sharing their raw data with a …

We consider federated learning in a wireless edge network, where multiple power-limited
mobile devices collaboratively train a global model, using their local data with the assistance …

This paper focuses on over-the-air federated learning (OTA-FL) for edge devices that have
non-independent and identically distributed (non-IID) datasets. Federated averaging …

With the growth of terminal devices and data traffic, privacy concerns have inspired an
innovative edge learning framework, called federated learning (FL). Over-the-air …

We consider online distributed optimization in a networked system, where multiple devices
assisted by a server collaboratively minimize the accumulation of a sequence of global loss …

This paper considers federated learning (FL) in a tactical network (TN), by adopting
computationally efficient over-the-air aggregation (OTA) to train a global model at a …

Federated learning (FL) with over-the-air computation can efficiently utilize the
communication bandwidth but is susceptible to analog aggregation error. Excluding those …

Combining over-the-air uplink transmission and multi-antenna beamforming can improve
the efficiency of federated learning (FL). However, to mitigate the significant aggregation …

In this thesis, I propose new online learning and optimization approaches to evaluate and
design communication networks by investigating unknown system variation, feedback delay …