232 citations · 453 across the 44 of their papers we have counts for
5 papers · 1 filter
Federated Expectation Maximization with heterogeneity mitigation and variance reduction
Aymeric Dieuleveut, Gersende Fort, Eric Moulines +1
The Expectation Maximization (EM) algorithm is the default algorithm for inference in latent variable models. As in any other field of machine learning, applications of latent vari…
Differentially Private Federated Learning on Heterogeneous Data
Maxence Noble, Aurélien Bellet, Aymeric Dieuleveut
Federated Learning (FL) is a paradigm for large-scale distributed learning which faces two key challenges: (i) efficient training from highly heterogeneous user data, and (ii) prot…
Super-Acceleration with Cyclical Step-sizes
Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut +2
We develop a convergence-rate analysis of momentum with cyclical step-sizes. We show that under some assumption on the spectral gap of Hessians in machine learning, cyclical step-s…
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning
Maxime Vono, Vincent Plassier, Alain Durmus +2
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 wh…
Preserved central model for faster bidirectional compression in distributed settings
Constantin Philippenko, Aymeric Dieuleveut
We develop a new approach to tackle communication constraints in a distributed learning problem with a central server. We propose and analyze a new algorithm that performs bidirect…