Reliability and robustness of federated learning in practical applications. (Fiabilité et robustesse de l'apprentissage fédéré pour applications concrètes).

[BibT_eX]

[DOI]

Yann Fraboni

PhD thesis, 2023

A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2023

Validation of Federated Unlearning on Collaborative Prostate Segmentation.

[BibT_eX]

[DOI]

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

2022

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

[BibT_eX]

[DOI]

CoRR, 2022

A General Theory for Client Sampling in Federated Learning.

[BibT_eX]

[DOI]

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

2021

On The Impact of Client Sampling on Federated Learning Convergence.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

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

Free-rider Attacks on Model Aggregation in Federated Learning.

[BibT_eX]

[DOI]

Yann Fraboni

Richard Vidal

Marco Lorenzi

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

Yann Fraboni

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