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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 ...
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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...
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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...
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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...
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06/21/2020
