Richard Vidal

According to our database, Richard Vidal authored at least 14 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

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

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, 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

CoRR, 2023

Validation of Federated Unlearning on Collaborative Prostate Segmentation.

Yann Fraboni, Lucia Innocenti, Michela Antonelli, Richard Vidal, Laetitia Kameni, Sébastien Ourselin, Marco Lorenzi

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops, 2023

Federated Learning for Data Streams.

Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

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

Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi

CoRR, 2022

A General Theory for Client Sampling in Federated Learning.

Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi

Proceedings of the Trustworthy Federated Learning - First International Workshop, 2022

Personalized Federated Learning through Local Memorization.

Othmane Marfoq, Giovanni Neglia, Richard Vidal, Laetitia Kameni

Proceedings of the International Conference on Machine Learning, 2022

2021

On The Impact of Client Sampling on Federated Learning Convergence.

Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi

CoRR, 2021

Federated Multi-Task Learning under a Mixture of Distributions.

Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi

Proceedings of the 38th International Conference on Machine Learning, 2021

Free-rider Attacks on Model Aggregation in Federated Learning.

Yann Fraboni, Richard Vidal, Marco Lorenzi

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Throughput-Optimal Topology Design for Cross-Silo Federated Learning.

Othmane Marfoq, Chuan Xu, Giovanni Neglia, Richard Vidal

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020