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"

5 / 5 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 80 0 0 31 Jan 2025
FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings Jean Ogier du Terrail Quentin Klopfenstein Honghao Li Imke Mayer Nicolas Loiseau Mohammad Hallal Félix Balazard M. Andreux 23 2 0 28 Nov 2023
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 47 144 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 48 91 0 05 Aug 2022
Stratified cross-validation for unbiased and privacy-preserving federated learning R. Bey Romain Goussault M. Benchoufi R. Porcher FedML 29 12 0 22 Jan 2020