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Publications of Yann Fraboni

Thesis

  1. Yann Fraboni. Reliability and robustness of federated learning in practical applications. Theses, Université Côte d'Azur, May 2023. Keyword(s): Federated learning, Heterogeneous data, Privacy, Distributed optimization, Bias, Apprentissage fédéré, Données hétérogènes, Protection des données, Optimisation distribuée, Biais. [bibtex-entry]


Articles in journal, book chapters

  1. Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi. A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. Journal of Machine Learning Research, 24:1-43, March 2023. [bibtex-entry]


Conference articles

  1. Yann Fraboni, Lucia Innocenti, Michela Antonelli, Richard Vidal, Laetitia Kameni, Sebastien Ourselin, and Marco Lorenzi. Validation of Federated Unlearning on Collaborative Prostate Segmentation. In DECAF MICCAI 2023 Workshops, volume 14393 of Lecture Notes in Computer Science, Toronto, Canada, pages 322-333, October 2023. Medical Image Computing and Computer Assisted Intervention, Springer Nature Switzerland. Keyword(s): federated unlearning, prostate cancer, segmentation, Medical imaging. [bibtex-entry]


  2. Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi. A General Theory for Client Sampling in Federated Learning. In International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI'22), Vienna, Austria, July 2022. [bibtex-entry]


  3. Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi. Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. In ICML 2021 - 38th International Conference on Machine Learning, online, United States, July 2021. Keyword(s): Client sampling, federated learning, sampling variance, data representativity. [bibtex-entry]


  4. Yann Fraboni, Richard Vidal, and Marco Lorenzi. Free-rider Attacks on Model Aggregation in Federated Learning. In AISTATS 2021 - 24th International Conference on Artificial Intelligence and Statistics, San Diego, United States, April 2021. [bibtex-entry]


Miscellaneous

  1. Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi. Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization. Note: Working paper or preprint, January 2023. [bibtex-entry]



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