Shielding Federated Learning Systems against Inference Attacks with ARM TrustZone

no code implementations • 11 Aug 2022 • Aghiles Ait Messaoud, Sonia Ben Mokhtar, Vlad Nitu, Valerio Schiavoni

Specifically, in FL, models are trained on the users devices and only model updates (i. e., gradients) are sent to a central server for aggregation purposes.

Federated Learning

PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning

no code implementations • 9 Aug 2022 • Yacine Belal, Aurélien Bellet, Sonia Ben Mokhtar, Vlad Nitu

To remedy this, we propose PEPPER, a decentralized recommender system based on gossip learning principles.

Federated Learning Movie Recommendation +1

Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments

no code implementations • 29 May 2021 • Lê-Nguyên Hoang, Louis Faucon, Aidan Jungo, Sergei Volodin, Dalia Papuc, Orfeas Liossatos, Ben Crulis, Mariame Tighanimine, Isabela Constantin, Anastasiia Kucherenko, Alexandre Maurer, Felix Grimberg, Vlad Nitu, Chris Vossen, Sébastien Rouault, El-Mahdi El-Mhamdi

We outline the structure of the Tournesol database, the key features of the Tournesol platform and the main hurdles that must be overcome to make it a successful project.

FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

no code implementations • 12 Jun 2020 • Georgios Damaskinos, Rachid Guerraoui, Anne-Marie Kermarrec, Vlad Nitu, Rhicheek Patra, Francois Taiani

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 local.

Federated Learning