research
∙
11/21/2022
Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization
The aim of Machine Unlearning (MU) is to provide theoretical guarantees ...
share
research
∙
06/21/2022
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates
We propose a novel framework to study asynchronous federated learning op...
research
∙
07/26/2021
On The Impact of Client Sampling on Federated Learning Convergence
While clients' sampling is a central operation of current state-of-the-a...
research
∙
05/12/2021
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
This work addresses the problem of optimizing communications between ser...
research
∙
06/21/2020