Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. - Yann Fraboni; Richard Vidal; Laetitia Kameni; Marco Lorenzi (ICML, 2021)

Federated Multi-Task Learning under a Mixture of Distributions. - Othmane Marfoq; Giovanni Neglia; Aurélien Bellet; Laetitia Kameni; Richard Vidal (NeurIPS, 2021)

On The Impact of Client Sampling on Federated Learning Convergence. - Yann Fraboni; Richard Vidal; Laetitia Kameni; Marco Lorenzi (CoRR, 2021)

Personalized Federated Learning through Local Memorization. - Othmane Marfoq; Giovanni Neglia; Richard Vidal; Laetitia Kameni (ICML, 2022)

A General Theory for Client Sampling in Federated Learning. - Yann Fraboni; Richard Vidal; Laetitia Kameni; Marco Lorenzi (FL@IJCAI, 2022)

Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization. - Yann Fraboni; Richard Vidal; Laetitia Kameni; Marco Lorenzi (CoRR, 2022)

Federated Learning for Data Streams. - Othmane Marfoq; Giovanni Neglia; Laetitia Kameni; Richard Vidal (AISTATS, 2023)

Validation of Federated Unlearning on Collaborative Prostate Segmentation. - Yann Fraboni; Lucia Innocenti; Michela Antonelli; Richard Vidal; Laetitia Kameni; Sébastien Ourselin; Marco Lorenzi (ISIC/Care-AI/MedAGI/DeCaF@MICCAI, 2023)

Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. - Francesco Cremonesi; Marc Vesin; Sergen Cansiz; Yannick Bouillard; Irene Balelli; Lucia Innocenti; Santiago Silva 001; Samy-Safwan Ayed; Riccardo Taiello; Laetitia Kameni; Richard Vidal; Fanny Orlhac; et al. (CoRR, 2023)

A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. - Yann Fraboni; Richard Vidal; Laetitia Kameni; Marco Lorenzi (J. Mach. Learn. Res., 2023)

SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization. - Yann Fraboni; Martin Van Waerebeke; Kevin Scaman; Richard Vidal; Laetitia Kameni; Marco Lorenzi (AISTATS, 2024)

Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments. - Charlotte Rodriguez; Laura Degioanni; Laetitia Kameni; Richard Vidal; Giovanni Neglia (CoRR, 2024)