Major bottlenecks of large-scale Federated Learning (FL) networks are the high costs for
communication and computation. This is due to the fact that most of current FL frameworks …

Federated learning (FL) is a decentralized algorithm that can train a globally shared model
without the requirement to send the raw data to a centralized server by user equipments …

Federated Learning (FL) is a promising decentralized machine learning technique, which
can be efficiently used to reduce the latency and deal with the data privacy in the next 6th …

This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the
superposition property of the wireless medium, and performs model aggregation over the air …

We study federated learning (FL), where power-limited wireless devices utilize their local
datasets to collaboratively train a global model with the help of a remote parameter server …

Federated learning (FL) is a swiftly evolving field within machine learning for collaboratively
training models at the network edge in a privacy-preserving fashion, without training data …

Federated learning (FL) has been widely regarded as a promising distributed machine
learning technology that utilizes on-device computation while protecting clients' data privacy …

We study federated learning (FL) with over-the-air aggregation over time-varying wireless
channels. Independent workers compute local gradients based on their local datasets and …

Federated learning has shown its advances recently but is still facing many challenges, such
as how algorithms save communication resources and reduce computational costs, and …

Federated learning (FL) and edge computing are both important technologies to support the
future Internet of Things (IoT). Despite that the network supported by edge computing has …