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2020 – today
- 2024
[c9]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: 3457-3465- [i11]Charlotte Rodriguez, Laura Degioanni, Laetitia Kameni, Richard Vidal, Giovanni Neglia:
Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments. CoRR abs/2408.15128 (2024) - 2023
- [j1]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: 110:1-110:43 (2023) - [c8]Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal:
Federated Learning for Data Streams. AISTATS 2023: 8889-8924 - [c7]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: 322-333 - [i10]Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal:
Federated Learning for Data Streams. CoRR abs/2301.01542 (2023) - [i9]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:
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. CoRR abs/2304.12012 (2023) - 2022
- [c6]Othmane Marfoq, Giovanni Neglia, Richard Vidal, Laetitia Kameni:
Personalized Federated Learning through Local Memorization. ICML 2022: 15070-15092 - [c5]Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi:
A General Theory for Client Sampling in Federated Learning. FL@IJCAI 2022: 46-58 - [i8]Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi:
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. CoRR abs/2206.10189 (2022) - [i7]Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi:
Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization. CoRR abs/2211.11656 (2022) - 2021
- [c4]Yann Fraboni, Richard Vidal, Marco Lorenzi:
Free-rider Attacks on Model Aggregation in Federated Learning. AISTATS 2021: 1846-1854 - [c3]Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi:
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. ICML 2021: 3407-3416 - [c2]Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal:
Federated Multi-Task Learning under a Mixture of Distributions. NeurIPS 2021: 15434-15447 - [i6]Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi:
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. CoRR abs/2105.05883 (2021) - [i5]Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi:
On The Impact of Client Sampling on Federated Learning Convergence. CoRR abs/2107.12211 (2021) - [i4]Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal:
Federated Multi-Task Learning under a Mixture of Distributions. CoRR abs/2108.10252 (2021) - [i3]Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal:
Personalized Federated Learning through Local Memorization. CoRR abs/2111.09360 (2021) - 2020
- [c1]Othmane Marfoq, Chuan Xu, Giovanni Neglia, Richard Vidal:
Throughput-Optimal Topology Design for Cross-Silo Federated Learning. NeurIPS 2020 - [i2]Yann Fraboni, Richard Vidal, Marco Lorenzi:
Free-rider Attacks on Model Aggregation in Federated Learning. CoRR abs/2006.11901 (2020) - [i1]Othmane Marfoq, Chuan Xu, Giovanni Neglia, Richard Vidal:
Throughput-Optimal Topology Design for Cross-Silo Federated Learning. CoRR abs/2010.12229 (2020)
Coauthor Index
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