T
Théo Ryffel
Researcher at École Normale Supérieure
Publications - 14
Citations - 872
Théo Ryffel is an academic researcher from École Normale Supérieure. The author has contributed to research in topics: Deep learning & Encryption. The author has an hindex of 7, co-authored 11 publications receiving 395 citations. Previous affiliations of Théo Ryffel include French Institute for Research in Computer Science and Automation.
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A generic framework for privacy preserving deep learning
Théo Ryffel,Andrew Trask,Morten Dahl,Bobby Wagner,Jason Mancuso,Daniel Rueckert,Jonathan Passerat-Palmbach +6 more
TL;DR: A new framework for privacy preserving deep learning that allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user is detailed.
Posted Content
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Miles Brundage,Shahar Avin,Jasmine Wang,Haydn Belfield,Gretchen Krueger,Gillian K. Hadfield,Gillian K. Hadfield,Heidy Khlaaf,Jingying Yang,Helen Toner,Ruth Fong,Tegan Maharaj,Pang Wei Koh,Sara Hooker,Jade Leung,Andrew Trask,Emma Bluemke,Jonathan Lebensbold,Cullen O'Keefe,Mark Koren,Théo Ryffel,J. B. Rubinovitz,Tamay Besiroglu,Federica Carugati,Jack Clark,Peter Eckersley,Sarah de Haas,Maritza Johnson,Ben Laurie,Alex Ingerman,Igor Krawczuk,Amanda Askell,Rosario Cammarota,Andrew J. Lohn,David Krueger,Charlotte Stix,Peter Henderson,Logan Graham,Carina E. A. Prunkl,Bianca Martin,Elizabeth Seger,Noa Zilberman,Seán Ó hÉigeartaigh,Frens Kroeger,Girish Sastry,Rebecca Kagan,Adrian Weller,Adrian Weller,Brian Tse,Elizabeth A. Barnes,Allan Dafoe,Paul Scharre,Ariel Herbert-Voss,Martijn Rasser,Shagun Sodhani,Carrick Flynn,Thomas Krendl Gilbert,Lisa Dyer,Saif Khan,Yoshua Bengio,Markus Anderljung +60 more
TL;DR: This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems.
Journal ArticleDOI
End-to-end privacy preserving deep learning on multi-institutional medical imaging
Georgios Kaissis,Alexander Ziller,Jonathan Passerat-Palmbach,Théo Ryffel,Dmitrii Usynin,Andrew Trask,Ionésio Lima,Jason Mancuso,Friederike Jungmann,Marc-Matthias Steinborn,Andreas Saleh,Marcus R. Makowski,Daniel Rueckert,Daniel Rueckert,Rickmer Braren,Rickmer Braren +15 more
TL;DR: PriMIA (Privacy-preserving Medical Image Analysis), a free, open-source software framework for differentially private, securely aggregated federated learning and encrypted inference on medical imaging data, is presented.
Book ChapterDOI
PySyft: A Library for Easy Federated Learning
Alexander Ziller,Andrew Trask,Antonio Lopardo,Benjamin Szymkow,Bobby Wagner,Emma Bluemke,Jean-Mickael Nounahon,Jonathan Passerat-Palmbach,Kritika Prakash,Nick Rose,Théo Ryffel,Zarreen Naowal Reza,Georgios Kaissis +12 more
TL;DR: This chapter introduces Duet: the authors' tool for easier FL for scientists and data owners and provides a proof-of-concept demonstration of a FL workflow using an example of how to train a convolutional neural network.
Posted Content
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
TL;DR: This work proposes AriaNN, a low-interaction privacy-preserving framework for private neural network training and inference on sensitive data, and implements the framework as an extensible system on top of PyTorch that leverages CPU and GPU hardware acceleration for cryptographic and machine learning operations.