Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning

25 October 2019

Papers citing "Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning"

6 / 6 papers shown

Title
FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning F. Stricker J. A. Peregrina D. Bermbach C. Zirpins FedML 167 0 0 31 Jan 2025
Federated Multilingual Models for Medical Transcript Analysis Andre Manoel Mirian Hipolito Garcia Tal Baumel Shize Su Jialei Chen Dan Miller D. Karmon Robert Sim Dimitrios Dimitriadis 61 13 0 04 Nov 2022
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings Jean Ogier du Terrail Samy Ayed Edwige Cyffers Felix Grimberg Chaoyang He ... Sai Praneeth Karimireddy Marco Lorenzi Giovanni Neglia Marc Tommasi M. Andreux FedML 133 158 0 10 Oct 2022
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions A. Rauniyar D. Hagos Debesh Jha J. E. Haakegaard Ulas Bagci D. Rawat Vladimir Vlassov OOD 155 105 0 05 Aug 2022
Privacy Intelligence: A Survey on Image Privacy in Online Social Networks Chi Liu Tianqing Zhu Jun Zhang Wanlei Zhou PICV 81 31 0 27 Aug 2020
Stratified cross-validation for unbiased and privacy-preserving federated learning R. Bey Romain Goussault M. Benchoufi R. Porcher FedML 66 12 0 22 Jan 2020