Federated learning encapsulates distributed learning strategies that are managed by a
central unit. Since it relies on using a selected number of agents at each iteration, and since …

Federated learning involves a mixture of centralized and decentralized processing tasks,
where a server regularly selects a sample of the agents and these in turn sample their local …

Federated Learning (FL) is popular for communication-efficient learning from distributed
data. To utilize data at different clients without moving them to the cloud, algorithms such as …

Federated learning (FL) is an emerging machine learning paradigm that allows to
accomplish model training without aggregating data at a central server. Most studies on FL …

Traditional federated learning algorithms impose strict requirements on the participation
rates of devices, which limit the potential reach of federated learning. This paper extends the …

Federated learning (FL) enables distributed model training from local data collected by
users. In distributed systems with constrained resources and potentially high dynamics, eg …

We propose a novel federated learning method for distributively training neural network
models, where the server orchestrates cooperation between a subset of randomly chosen …

We demonstrate that merely analog transmissions and match filtering can realize the
function of an edge server in federated learning (FL). Therefore, a network with massively …

Federated learning (FL) is a machine learning paradigm that targets model training without
gathering the local data dispersed over various data sources. Standard FL, which employs a …

This paper introduces Distributed Stein Variational Gradient Descent (DSVGD), a non-
parametric generalized Bayesian inference framework for federated learning. DSVGD …