Privacy Preserving Reinforcement Learning over Distributed Datasets. - Regis Loeb; Timothy Verstraeten; Ann Nowé; Ann Dooms (BNAIC/BENELEARN, 2019)

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. - Jean Ogier du Terrail; Samy-Safwan Ayed; Edwige Cyffers; Felix Grimberg; Chaoyang He 001; Regis Loeb; Paul Mangold; Tanguy Marchand; Othmane Marfoq; Erum Mushtaq; Boris Muzellec; Constantin Philippenko; et al. (NeurIPS, 2022)

SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning. - Tanguy Marchand; Regis Loeb; Ulysse Marteau-Ferey; Jean Ogier du Terrail; Arthur Pignet (ICML, 2023)

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information. - Wouter Heyndrickx; Lewis H. Mervin; Tobias Morawietz; Noé Sturm; Lukas Friedrich; Adam Zalewski; Anastasia Pentina; Lina Humbeck; Martijn Oldenhof; Ritsuya Niwayama; Peter Schmidtke; Nikolas Fechner; et al. (J. Chem. Inf. Model., 2024)