no code implementations • 6 Feb 2024 • Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, REDA ALAMI, Alexey Naumov, Eric Moulines
In this paper, we perform a non-asymptotic analysis of the federated linear stochastic approximation (FedLSA) algorithm.
no code implementations • 29 Aug 2023 • Hadrien Hendrikx, Paul Mangold, Aurélien Bellet
Leveraging this assumption, we introduce the Relative Gaussian Mechanism (RGM), in which the variance of the noise depends on the norm of the output.
1 code implementation • 28 Oct 2022 • Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi
We theoretically study the impact of differential privacy on fairness in classification.
1 code implementation • 10 Oct 2022 • Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Teleńczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
no code implementations • 4 Jul 2022 • Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi
In this paper, we study differentially private empirical risk minimization (DP-ERM).
no code implementations • 22 Oct 2021 • Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi
In this paper, we propose Differentially Private proximal Coordinate Descent (DP-CD), a new method to solve composite DP-ERM problems.