9 papers
Robust Federated Inference
Akash Dhasade, Sadegh Farhadkhani, Rachid Guerraoui +4
Federated inference, in the form of one-shot federated learning, edge ensembles, or federated ensembles, has emerged as an attractive solution to combine predictions from multiple…
Privacy Amplification by Missing Data
Simon Roburin, Rafaël Pinot, Erwan Scornet
Privacy preservation is a fundamental requirement in many high-stakes domains such as medicine and finance, where sensitive personal data must be analyzed without compromising indi…
On the Inherent Anonymity of Gossiping
Rachid Guerraoui, Anne-Marie Kermarrec, Anastasiia Kucherenko +2
Detecting the source of a gossip is a critical issue, related to identifying patient zero in an epidemic, or the origin of a rumor in a social network. Although it is widely acknow…
Fairness Meets Privacy: Integrating Differential Privacy and Demographic Parity in Multi-class Classification
Lilian Say, Christophe Denis, Rafael Pinot
The increasing use of machine learning in sensitive applications demands algorithms that simultaneously preserve data privacy and ensure fairness across potentially sensitive sub-p…
Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory
Lucas Gnecco-Heredia, Matteo Sammut, Muni Sreenivas Pydi +3
Randomization as a mean to improve the adversarial robustness of machine learning models has recently attracted significant attention. Unfortunately, much of the theoretical analys…
ByzFL: Research Framework for Robust Federated Learning
Marc González, Rachid Guerraoui, Rafael Pinot +3
We present ByzFL, an open-source Python library for developing and benchmarking robust federated learning (FL) algorithms. ByzFL provides a unified and extensible framework that in…