The canonical formulation of federated learning treats it as a distributed optimization
problem where the model parameters are optimized against a global loss function that …

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

Bayesian Federated Learning (FL) offers a principled framework to account for the
uncertainty caused by limitations in the data available at the nodes implementing …

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 …

Bayesian Federated Learning (FL) has been recently introduced to provide well-calibrated
Machine Learning (ML) models quantifying the uncertainty of their predictions. Despite their …

Learning a privacy-preserving model from sensitive data which are distributed across
multiple devices is an increasingly important problem. The problem is often formulated in the …

Abstract Recent advancements in Large Language Models and Retrieval-Augmented
Generation have boosted interest in domain-specific question-answering for enterprise …

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 …

Deep neural networks (DNNs) have achieved tremendous success in recent years due to
their ability to learn from large datasets. Such ability is facilitated by the substantial number …