Personalised federated learning (FL) aims at collaboratively learning a machine learning
model tailored for each client. Albeit promising advances have been made in this direction …

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

Compression schemes have been extensively used in Federated Learning (FL) to reduce
the communication cost of distributed learning. While most approaches rely on a bounded …

Bayesian personalized federated learning (BPFL) addresses challenges in existing
personalized FL (PFL). BPFL aims to quantify the uncertainty and heterogeneity within and …

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 …

Variational particle-based Bayesian learning methods have the advantage of not being
limited by the bias affecting more conventional parametric techniques. This paper proposes …

Abstract Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel
computing to scale Bayesian inference to large datasets by using a two-step approach. First …

We propose a federated averaging Langevin algorithm (FA-LD) for uncertainty quantification
and mean predictions with distributed clients. In particular, we generalize beyond normal …

Federated sampling algorithms have recently gained great popularity in the community of
machine learning and statistics. This paper studies variants of such algorithms called Error …

Low-precision training has emerged as a promising low-cost technique to enhance the
training efficiency of deep neural networks without sacrificing much accuracy. Its Bayesian …