3 papers
cs.CR2026
Giskard : Byzantine Robust and Private Aggregation for Large-Scale Decentralized Learning
Ousmane Touat, César Sabater, Mohamed Maouche +1
In decentralized learning, clients train a machine learning model while keeping their data locally and share their model parameters or gradients with a set of neighbors, making it…
cs.LG2024
Exposing the Vulnerability of Decentralized Learning to Membership Inference Attacks Through the Lens of Graph Mixing
Ousmane Touat, Jezekael Brunon, Yacine Belal +4
The primary promise of decentralized learning is to allow users to engage in the training of machine learning models in a collaborative manner while keeping their data on their pre…
cs.LG2023
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators
Ousmane Touat, Julian Stier, Pierre-Edouard Portier +1
A wide variety of generative models for graphs have been proposed. They are used in drug discovery, road networks, neural architecture search, and program synthesis. Generating gra…