94 citations · 344 across the 54 of their papers we have counts for
6 papers · 2 filters
Differential Privacy has Bounded Impact on Fairness in Classification
Paul Mangold, Michaël Perrot, Aurélien Bellet +1
We theoretically study the impact of differential privacy on fairness in classification. We prove that, given a class of models, popular group fairness measures are pointwise Lipsc…
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
Collaborative Algorithms for Online Personalized Mean Estimation
Mahsa Asadi, Aurélien Bellet, Odalric-Ambrym Maillard +1
We consider an online estimation problem involving a set of agents. Each agent has access to a (personal) process that generates samples from a real-valued distribution and seeks t…
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
Paul Mangold, Aurélien Bellet, Joseph Salmon +1
In this paper, we study differentially private empirical risk minimization (DP-ERM). It has been shown that the worst-case utility of DP-ERM reduces polynomially as the dimension i…
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data
Batiste Le Bars, Aurélien Bellet, Marc Tommasi +2
One of the key challenges in decentralized and federated learning is to design algorithms that efficiently deal with highly heterogeneous data distributions across agents. In this…
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation
Sina Sajadmanesh, Ali Shahin Shamsabadi, Aurélien Bellet +1
In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Pert…