30 citations · 83 across the 5 of their papers we have counts for
5 papers
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…
Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments
Lê-Nguyên Hoang, Louis Faucon, Aidan Jungo +13
Today's large-scale algorithms have become immensely influential, as they recommend and moderate the content that billions of humans are exposed to on a daily basis. They are the d…
FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction
Georgios Damaskinos, Rachid Guerraoui, Anne-Marie Kermarrec +3
Federated Learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users l…
Efficient, Dynamic Multi-tenant Edge Computation in EdgeOS
Yuxin Ren, Vlad Nitu, Guyue Liu +4
In the future, computing will be immersed in the world around us -- from augmented reality to autonomous vehicles to the Internet of Things. Many of these smart devices will offer…