| 2024 | AISTATS | 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 |
| 2023 | AISTATS | Federated Learning for Data Streams. | Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal |
| 2023 | MICCAI | Validation of Federated Unlearning on Collaborative Prostate Segmentation. | Yann Fraboni, Lucia Innocenti, Michela Antonelli, Richard Vidal, Laetitia Kameni, Sbastien Ourselin, Marco Lorenzi |
| 2022 | ICML | Personalized Federated Learning through Local Memorization. | Othmane Marfoq, Giovanni Neglia, Richard Vidal, Laetitia Kameni |
| 2022 | IJCAI | A General Theory for Client Sampling in Federated Learning. | Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi |
| 2021 | AISTATS | Free-rider Attacks on Model Aggregation in Federated Learning. | Yann Fraboni, Richard Vidal, Marco Lorenzi |
| 2021 | ICML | Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. | Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi |