A Decentralized Framework for Biostatistics and Privacy Concerns. - Paul Mangold; Alexandre Filiot; Mouhamed Moussa; Vincent Sobanski; Grégoire Ficheur; Paul Andrey; Antoine Lamer (EFMI-STC, 2020)

Specifications for the Routine Implementation of Federated Learning in Hospitals Networks. - Antoine Lamer; Alexandre Filiot; Yannick Bouillard; Paul Mangold; Paul Andrey; Jessica Schiro (MIE, 2021)

Differentially Private Coordinate Descent for Composite Empirical Risk Minimization. - Paul Mangold; Aurélien Bellet; Joseph Salmon; Marc Tommasi (ICML, 2022)

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)

Fairness Certificates for Differentially Private Classification. - Paul Mangold; Michaël Perrot; Aurélien Bellet; Marc Tommasi (CoRR, 2022)

High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent. - Paul Mangold; Aurélien Bellet; Joseph Salmon; Marc Tommasi (AISTATS, 2023)

Differential Privacy has Bounded Impact on Fairness in Classification. - Paul Mangold; Michaël Perrot; Aurélien Bellet; Marc Tommasi (ICML, 2023)

Exploiting Problem Structure in Privacy-Preserving Optimization and Machine Learning. (Exploitation de la Structure des Problèmes en Optimisation et en Apprentissage Automatique Respectueux de la Vie Privée). - Paul Mangold (2023)

The Relative Gaussian Mechanism and its Application to Private Gradient Descent. - Hadrien Hendrikx; Paul Mangold; Aurélien Bellet (AISTATS, 2024)

SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning. - Paul Mangold; Sergey Samsonov; Safwan Labbi; Ilya Levin; Réda Alami; Alexey Naumov; Eric Moulines (NeurIPS, 2024)

SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning. - Paul Mangold; Sergey Samsonov; Safwan Labbi; Ilya Levin; Réda Alami; Alexey Naumov; Eric Moulines (CoRR, 2024)

Joint Channel Selection using FedDRL in V2X. - Lorenzo Mancini; Safwan Labbi; Karim Abed-Meraim; Fouzi Boukhalfa; Alain Durmus; Paul Mangold; Eric Moulines (CoRR, 2024)

Refined Analysis of Federated Averaging's Bias and Federated Richardson-Romberg Extrapolation. - Paul Mangold; Alain Durmus; Aymeric Dieuleveut; Sergey Samsonov; Eric Moulines (CoRR, 2024)

Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents. - Safwan Labbi; Daniil Tiapkin; Lorenzo Mancini; Paul Mangold; Eric Moulines (AISTATS, 2025)

Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation. - Paul Mangold; Alain Oliviero Durmus; Aymeric Dieuleveut; Sergey Samsonov; Eric Moulines (AISTATS, 2025)

Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up. - Paul Mangold; Alain Durmus; Aymeric Dieuleveut; Eric Moulines (CoRR, 2025)

On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment. - Safwan Labbi; Paul Mangold; Daniil Tiapkin; Eric Moulines (CoRR, 2025)