Free-rider Attacks on Model Aggregation in Federated Learning. - Yann Fraboni; Richard Vidal; Marco Lorenzi (AISTATS, 2021)

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

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

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)

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)

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)

Reliability and robustness of federated learning in practical applications. (Fiabilité et robustesse de l'apprentissage fédéré pour applications concrètes). - Yann Fraboni (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)