Most works on federated learning (FL) focus on the most common frequentist formulation of
learning whereby the goal is minimizing the global empirical loss. Frequentist learning …

For engineering applications of artificial intelligence, Bayesian learning holds significant
advantages over standard frequentist learning, including the capacity to quantify uncertainty …

Federated learning is a privacy-preserving and distributed training method using
heterogeneous data sets stored at local devices. Federated learning over wireless networks …

Federated learning (FL) has achieved great success as a privacy-preserving distributed
training paradigm, where many edge devices collaboratively train a machine learning model …

Probabilistic predictions with machine learning are important in many applications. These
are commonly done with Bayesian learning algorithms. However, Bayesian learning …

Abstract The objective of Federated Learning (FL) is to perform statistical inference for data
which are decentralised and stored locally on networked clients. FL raises many constraints …

Federated learning (FL) has attracted much attention as a privacy-preserving distributed
machine learning framework, where many clients collaboratively train a machine learning …

Federated Learning (FL) presents a mechanism to allow decentralized training for machine
learning (ML) models inherently enabling privacy preservation. The classical FL is …

Federated Learning (FL) refers to distributed protocols that avoid direct raw data exchange
among the participating devices while training for a common learning task. This way, FL can …

In federated learning (FL), model training is distributed over clients and local models are
aggregated by a central server. The performance of uploaded models in such situations can …