https://github.com/accenture/labs-federated-learning

Accenture Labs Federated Learning
https://github.com/accenture/labs-federated-learning

Last synced: 4 months ago
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Accenture Labs Federated Learning

• Host: GitHub
• URL: https://github.com/accenture/labs-federated-learning
• Owner: Accenture
• Created: 2020-04-23T06:57:45.000Z (about 6 years ago)
• Default Branch: landing_page
• Last Pushed: 2024-03-13T10:14:51.000Z (over 2 years ago)
• Last Synced: 2026-01-29T19:51:45.679Z (5 months ago)
• Size: 10.3 MB
• Stars: 106
• Watchers: 14
• Forks: 30
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Accenture Labs Sophia Antipolis Publications and Associated Code on Federated Learning

- Yann Fraboni, Richard Vidal, Marco Lorenzi. Free-rider Attacks on Model Aggregation in Federated Learning. *AISTATS 2021*

    - The paper can be found [here](http://proceedings.mlr.press/v130/fraboni21a.html).

    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/free-rider_attacks).   

- Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi. Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. *ICML 2021*

    - The paper can be found [here](http://proceedings.mlr.press/v139/fraboni21a.html).

    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/clustered_sampling).  

- Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi. A General Theory for Client Sampling in Federated Learning. *FL-IJCAI'22*

    - The paper can be found [here](https://arxiv.org/abs/2107.12211).

    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/impact_client_sampling).

- Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi. A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. *JMLR 2023*

    - The paper can be found [here](https://arxiv.org/abs/2206.10189).

    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/asynchronous_FL).

- Yann Fraboni, Martin Van Waerebeke, Richard Vidal, Laetitia Kameni, Kevin Scaman, Marco Lorenzi. Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization. Preprint.

    - The paper can be found [here](https://arxiv.org/abs/2211.11656).

    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/SIFU).

- Yann Fraboni, Lucia Innocenti,Michela Antonelli, Richard Vidal, Laetitia Kameni, Sebastien Ourselin, Marco Lorenzi. Validation of Federated Learning on Collaborative Prostate Segmentation. *MICCAI 2023 - DeCaF*

    - The paper can be found [here]().

    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/FU_prostate_segmentation).