Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure,
communication, computing and learning in a privacy-preserving manner. However, the …

Most existing federated learning methods are unable to estimate model/predictive
uncertainty since the client models are trained using the standard loss function minimization …

Standard federated learning (FL) algorithms typically require multiple rounds of
communication between the server and the clients, which has several drawbacks including …

The federated learning paradigm has motivated the development of methods for
aggregating multiple client updates into a global server model, without sharing client data …

Federated learning methods enable model training across distributed data sources without
data leaving their original locations and have gained increasing interest in various fields …

We propose a novel hierarchical Bayesian approach to Federated Learning (FL), where our
model reasonably describes the generative process of clients' local data via hierarchical …

Optimization-based techniques for federated learning (FL) often come with prohibitive
communication cost, as high dimensional model parameters need to be communicated …

While deep neural networks (DNNs) based personalized federated learning (PFL) is
demanding for addressing data heterogeneity and shows promising performance, existing …

Bayesian data fusion for distributed learning by Peng Wu Doctor of Philosophy in Electrical
and Computer Engineering Northeastern University, April 2024 Prof. Pau Closas, Advisor …