Laetitia Kameni

发表

A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates

Yann Fraboni, Richard Vidal, Laetitia Kameni, 2022, 2206.10189.

On The Impact of Client Sampling on Federated Learning Convergence

Yann Fraboni, Richard Vidal, Laetitia Kameni, 2021, ArXiv.

A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates

Yann Fraboni, Richard Vidal, Laetitia Kameni, 2022, J. Mach. Learn. Res..

A General Theory for Client Sampling in Federated Learning

Yann Fraboni, Richard Vidal, Laetitia Kameni, 2021, FL@IJCAI.

Personalized Federated Learning through Local Memorization

G. Neglia, Richard Vidal, Laetitia Kameni, 2021, ICML.

Federated Multi-Task Learning under a Mixture of Distributions pdf

Giovanni Neglia, Aurélien Bellet, Othmane Marfoq, 2021, NeurIPS.

Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization

Yann Fraboni, Richard Vidal, Laetitia Kameni, 2022, ArXiv.

Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning

Yann Fraboni, Marco Lorenzi, Laetitia Kameni, 2021, ICML.

Federated Learning for Data Streams

G. Neglia, Richard Vidal, Laetitia Kameni, 2023, AISTATS.

Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization

Yann Fraboni, Richard Vidal, Laetitia Kameni, 2022, ArXiv.