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A General Theory for Client Sampling in Federated Learning

 Yann Fraboni , Richard Vidal , Laetitia Kameni , Marco Lorenzi
International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI'22), Jul 2022, Vienna, Austria
Communication dans un congrès hal-03500307v2
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Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization

 Yann Fraboni , Richard Vidal , Laetitia Kameni , Marco Lorenzi
2023
Pré-publication, Document de travail hal-03910848v1
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Free-rider Attacks on Model Aggregation in Federated Learning

 Yann Fraboni , Richard Vidal , Marco Lorenzi
AISTATS 2021 - 24th International Conference on Artificial Intelligence and Statistics, Apr 2021, San Diego, United States
Communication dans un congrès hal-03123638v1
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Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning

 Yann Fraboni , Richard Vidal , Laetitia Kameni , Marco Lorenzi
ICML 2021 - 38th International Conference on Machine Learning, Jul 2021, online, United States
Communication dans un congrès hal-03232421v1
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A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates

 Yann Fraboni , Richard Vidal , Laetitia Kameni , Marco Lorenzi
Journal of Machine Learning Research, 2023, 24, pp.1-43
Article dans une revue hal-03720629v1
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Reliability and robustness of federated learning in practical applications

 Yann Fraboni
Artificial Intelligence [cs.AI]. Université Côte d'Azur, 2023. English. ⟨NNT : 2023COAZ4033⟩
Thèse tel-04141520v1
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