Richard Vidal - RankMe

21 papers A* 4A 3Journal 12Unranked 2

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments. CoRR Charlotte Rodriguez, Laura Degioanni, Laetitia Kameni, Richard Vidal, Giovanni Neglia
2024	A	`conf`	SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization. AISTATS Yann Fraboni, Martin Van Waerebeke, Kevin Scaman, Richard Vidal, Laetitia Kameni, Marco Lorenzi
2023	J	`jnl`	A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. J. Mach. Learn. Res. Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
2023	J	`jnl`	Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. CoRR 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
2023	A	`conf`	Federated Learning for Data Streams. AISTATS Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal
2023	J	`jnl`	Federated Learning for Data Streams. CoRR Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal
2023	—	`conf`	Validation of Federated Unlearning on Collaborative Prostate Segmentation. ISIC/Care-AI/MedAGI/DeCaF@MICCAI Yann Fraboni, Lucia Innocenti, Michela Antonelli, Richard Vidal, Laetitia Kameni, Sébastien Ourselin, Marco Lorenzi
2022	—	`conf`	A General Theory for Client Sampling in Federated Learning. FL@IJCAI Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
2022	J	`jnl`	A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. CoRR Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
2022	A*	`conf`	Personalized Federated Learning through Local Memorization. ICML Othmane Marfoq, Giovanni Neglia, Richard Vidal, Laetitia Kameni
2022	J	`jnl`	Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization. CoRR Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
2021	A*	`conf`	Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. ICML Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
2021	J	`jnl`	Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. CoRR Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
2021	A*	`conf`	Federated Multi-Task Learning under a Mixture of Distributions. NeurIPS Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal
2021	J	`jnl`	Federated Multi-Task Learning under a Mixture of Distributions. CoRR Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal
2021	A	`conf`	Free-rider Attacks on Model Aggregation in Federated Learning. AISTATS Yann Fraboni, Richard Vidal, Marco Lorenzi
2021	J	`jnl`	On The Impact of Client Sampling on Federated Learning Convergence. CoRR Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
2021	J	`jnl`	Personalized Federated Learning through Local Memorization. CoRR Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal
2020	J	`jnl`	Free-rider Attacks on Model Aggregation in Federated Learning. CoRR Yann Fraboni, Richard Vidal, Marco Lorenzi
2020	A*	`conf`	Throughput-Optimal Topology Design for Cross-Silo Federated Learning. NeurIPS Othmane Marfoq, Chuan Xu, Giovanni Neglia, Richard Vidal
2020	J	`jnl`	Throughput-Optimal Topology Design for Cross-Silo Federated Learning. CoRR Othmane Marfoq, Chuan Xu, Giovanni Neglia, Richard Vidal