94 citations · 252 across the 11 of their papers we have counts for
4 papers · 1 filter
Personalized Federated Learning through Local Memorization
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni +1
Federated learning allows clients to collaboratively learn statistical models while keeping their data local. Federated learning was originally used to train a unique global model…
Federated Multi-Task Learning under a Mixture of Distributions
Othmane Marfoq, Giovanni Neglia, Aurélien Bellet +2
The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL), a framework for on-device collaborative training of machi…
A General Theory for Client Sampling in Federated Learning
Yann Fraboni, Richard Vidal, Laetitia Kameni +1
While client sampling is a central operation of current state-of-the-art federated learning (FL) approaches, the impact of this procedure on the convergence and speed of FL remains…
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
Yann Fraboni, Richard Vidal, Laetitia Kameni +1
This work addresses the problem of optimizing communications between server and clients in federated learning (FL). Current sampling approaches in FL are either biased, or non opti…