30 citations · 31 across the 5 of their papers we have counts for
5 papers
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…
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…