Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial
participation) when the number of participants is large and the server's communication …

Federated Learning is a machine learning scheme in which a shared prediction model can
be collaboratively learned by a number of distributed nodes using their locally stored data. It …

Due to the dynamics of wireless channels and limited wireless resources (ie, spectrum),
deploying federated learning (FL) over wireless networks is challenged by frequent FL …

This paper presents a novel framework that enables the generation of unbiased estimates
for test loss using fewer labeled samples, effectively evaluating the predictive performance …

Federated learning systems facilitate the training of global models across large numbers of
distributed edge-devices with potentially heterogeneous data. Such systems operate in …

Federated learning involves a central processor that interacts with multiple agents to
determine a global model. The process consists of repeatedly exchanging estimates, which …

Federated learning can achieve multi-party data-collaborative applications while
safeguarding personal privacy. However, the process often leads to a decline in the quality …

As a promising learning paradigm integrating computation and communication, federated
learning (FL) proceeds the local training and the periodic sharing from distributed clients …

We consider the problem of information aggregation in federated decision making, where a
group of agents collaborate to infer the underlying state of nature without sharing their …

Federated learning (FL) is an emerging machine learning (ML) paradigm used to train
models across multiple nodes (ie, clients) holding local data sets, without explicitly …