Richard Vidal

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 20

Top Topics

Prostate Segmentation

Federated Databases

Learning Process

Top Venues

Publications

Yann Fraboni, Martin Van Waerebeke, Kevin Scaman, Richard Vidal, Laetitia Kameni, Marco Lorenzi
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization. AISTATS (2024)
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal
Federated Learning for Data Streams. CoRR (2023)
Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. J. Mach. Learn. Res. 24 (2023)
Yann Fraboni, Lucia Innocenti, Michela Antonelli, Richard Vidal, Laetitia Kameni, Sébastien Ourselin, Marco Lorenzi
Validation of Federated Unlearning on Collaborative Prostate Segmentation. ISIC/Care-AI/MedAGI/DeCaF@MICCAI (2023)
Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Santiago S. Silva R., Samy-Safwan Ayed, Riccardo Taiello, Laetitia Kameni, Richard Vidal, Fanny Orlhac, Christophe Nioche, Nathan Lapel, Bastien Houis, Romain Modzelewski, Olivier Humbert, Melek Önen, Marco Lorenzi
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. CoRR (2023)
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal
Federated Learning for Data Streams. AISTATS (2023)
Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. CoRR (2022)
Othmane Marfoq, Giovanni Neglia, Richard Vidal, Laetitia Kameni
Personalized Federated Learning through Local Memorization. ICML (2022)
Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
A General Theory for Client Sampling in Federated Learning. FL@IJCAI (2022)
Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization. CoRR (2022)
Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. CoRR (2021)
Yann Fraboni, Richard Vidal, Marco Lorenzi
Free-rider Attacks on Model Aggregation in Federated Learning. AISTATS (2021)
Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal
Federated Multi-Task Learning under a Mixture of Distributions. NeurIPS (2021)
Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
On The Impact of Client Sampling on Federated Learning Convergence. CoRR (2021)
Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. ICML (2021)
Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal
Federated Multi-Task Learning under a Mixture of Distributions. CoRR (2021)
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal
Personalized Federated Learning through Local Memorization. CoRR (2021)
Yann Fraboni, Richard Vidal, Marco Lorenzi
Free-rider Attacks on Model Aggregation in Federated Learning. CoRR (2020)
Othmane Marfoq, Chuan Xu, Giovanni Neglia, Richard Vidal
Throughput-Optimal Topology Design for Cross-Silo Federated Learning. NeurIPS (2020)
Othmane Marfoq, Chuan Xu, Giovanni Neglia, Richard Vidal
Throughput-Optimal Topology Design for Cross-Silo Federated Learning. CoRR (2020)