This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …

Federated learning (FL) has emerged as a promising paradigm for collaborative model
training while preserving data privacy. Client selection plays a crucial role in determining the …

Federated learning is a distributed machine learning framework that enables distributed
nodes with computation and storage capabilities to train a global model while keeping …

In federated learning, clients cooperatively train a global model by training local models over
their datasets under the coordination of a central server. However, clients may sometimes be …

In federated learning, a server must periodically broadcast a model to the agents. We
propose to use multi-resolution coding and modulation (also known as non-uniform …

Federated Learning (FL) is a distributed approach where numerous devices train a shared
global model for Machine Learning (ML) tasks. At every training round, the client devices …

The training of machine learning (ML) models usually requires a massive amount of data.
Nowadays, the ever-increasing number of connected user devices has benefited the …

Federated learning is a distributed machine learning approach that keeps data locally while
achieving the utilization of fragmented data and protecting client privacy to a certain extent …

Heterogeneous federated learning without assuming any structure is challenging due to the
conflicts among non-identical data distributions of clients. In practice, clients often comprise …