no code implementations • 5 Jun 2023 • Batiste Le Bars, Aurélien Bellet, Marc Tommasi
This paper presents a new generalization error analysis for the Decentralized Stochastic Gradient Descent (D-SGD) algorithm based on algorithmic stability.
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.
1 code implementation • 24 Aug 2022 • Mahsa Asadi, Aurélien Bellet, Odalric-Ambrym Maillard, Marc Tommasi
We study the case where some of the distributions have the same mean, and the agents are allowed to actively query information from other agents.
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 • 9 Apr 2022 • Batiste Le Bars, Aurélien Bellet, Marc Tommasi, Erick Lavoie, Anne-Marie Kermarrec
One of the key challenges in decentralized and federated learning is to design algorithms that efficiently deal with highly heterogeneous data distributions across agents.
no code implementations • 23 Feb 2022 • Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurélien Bellet, Nathalie Vauquier, Emmanuel Vincent, Mohamed Maouche, Marc Tommasi, Nicolas Papernot
We remove speaker information from these attributes by introducing differentially private feature extractors based on an autoencoder and an automatic speech recognizer, respectively, trained using noise layers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
no code implementations • 7 Nov 2021 • Salima Mdhaffar, Jean-François Bonastre, Marc Tommasi, Natalia Tomashenko, Yannick Estève
The widespread of powerful personal devices capable of collecting voice of their users has opened the opportunity to build speaker adapted speech recognition system (ASR) or to participate to collaborative learning of ASR.
no code implementations • 6 Nov 2021 • Natalia Tomashenko, Salima Mdhaffar, Marc Tommasi, Yannick Estève, Jean-François Bonastre
This paper investigates methods to effectively retrieve speaker information from the personalized speaker adapted neural network acoustic models (AMs) in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
no code implementations • 18 May 2020 • Brij Mohan Lal Srivastava, Natalia Tomashenko, Xin Wang, Emmanuel Vincent, Junichi Yamagishi, Mohamed Maouche, Aurélien Bellet, Marc Tommasi
The recently proposed x-vector based anonymization scheme converts any input voice into that of a random pseudo-speaker.
no code implementations • 12 Nov 2019 • Brij Mohan Lal Srivastava, Aurélien Bellet, Marc Tommasi, Emmanuel Vincent
In this paper, we focus on the protection of speaker identity and study the extent to which users can be recognized based on the encoded representation of their speech as obtained by a deep encoder-decoder architecture trained for ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
no code implementations • 10 Nov 2019 • Brij Mohan Lal Srivastava, Nathalie Vauquier, Md Sahidullah, Aurélien Bellet, Marc Tommasi, Emmanuel Vincent
In this paper, we investigate anonymization methods based on voice conversion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
1 code implementation • 24 Jan 2019 • Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi
We consider the fully decentralized machine learning scenario where many users with personal datasets collaborate to learn models through local peer-to-peer exchanges, without a central coordinator.
no code implementations • 23 May 2017 • Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, Marc Tommasi
The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements.
no code implementations • 17 Oct 2016 • Paul Vanhaesebrouck, Aurélien Bellet, Marc Tommasi
We consider a set of learning agents in a collaborative peer-to-peer network, where each agent learns a personalized model according to its own learning objective.
no code implementations • NeurIPS 2012 • Antonino Freno, Mikaela Keller, Marc Tommasi
Statistical models for networks have been typically committed to strong prior assumptions concerning the form of the modeled distributions.