30 citations · 90 across the 12 of their papers we have counts for
12 papers
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…
DOME: Improving Signal-to-Noise in Stochastic Gradient Descent via Sharp-Direction Subspace Filtering
Julien Nicolas, Mohamed Maouche, Sonia Ben Mokhtar +1
Stochastic gradients for deep neural networks exhibit strong correlations along the optimization trajectory, and are often aligned with a small set of Hessian eigenvectors associat…
GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework
Yacine Belal, Mohamed Maouche, Sonia Ben Mokhtar
Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighboring peers. Recent approaches rely on dyn…
TEE-based Key-Value Stores: a Survey
Aghiles Ait Messaoud, Sonia Ben Mokhtar, Anthony Simonet-Boulogne
Key-Value Stores (KVSs) are No-SQL databases that store data as key-value pairs and have gained popularity due to their simplicity, scalability, and fast retrieval capabilities. Ho…
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…
Inferring Communities of Interest in Collaborative Learning-based Recommender Systems
Yacine Belal, Sonia Ben Mokhtar, Mohamed Maouche +1
Collaborative-learning-based recommender systems, such as those employing Federated Learning (FL) and Gossip Learning (GL), allow users to train models while keeping their history…
Survey of Federated Learning Models for Spatial-Temporal Mobility Applications
Yacine Belal, Sonia Ben Mokhtar, Hamed Haddadi +2
Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local. Federated Learning (FL) can serve as an i…
PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning
Yacine Belal, Aurélien Bellet, Sonia Ben Mokhtar +1
Recommender systems are proving to be an invaluable tool for extracting user-relevant content helping users in their daily activities (e.g., finding relevant places to visit, conte…
Shielding Federated Learning Systems against Inference Attacks with ARM TrustZone
Aghiles Ait Messaoud, Sonia Ben Mokhtar, Vlad Nitu +1
Federated Learning (FL) opens new perspectives for training machine learning models while keeping personal data on the users premises. Specifically, in FL, models are trained on th…
Adaptive Location Privacy with ALP
Vincent Primault, Antoine Boutet, Sonia Ben Mokhtar +1
With the increasing amount of mobility data being collected on a daily basis by location-based services (LBSs) comes a new range of threats for users, related to the over-sharing o…
Privacy-preserving Publication of Mobility Data with High Utility
Vincent Primault, Sonia Ben Mokhtar, Lionel Brunie
An increasing amount of mobility data is being collected every day by different means, e.g., by mobile phone operators. This data is sometimes published after the application of si…
Differentially Private Location Privacy in Practice
Vincent Primault, Sonia Ben Mokhtar, Cedric Lauradoux +1
With the wide adoption of handheld devices (e.g. smartphones, tablets) a large number of location-based services (also called LBSs) have flourished providing mobile users with real…