Artemis: tight convergence guarantees for bidirectional compression in Federated Learning. - Constantin Philippenko; Aymeric Dieuleveut (CoRR, 2020)

Preserved central model for faster bidirectional compression in distributed settings. - Constantin Philippenko; Aymeric Dieuleveut (NeurIPS, 2021)

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)

Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning. - Constantin Philippenko; Aymeric Dieuleveut (CoRR, 2023)

Compression bidirectionnelle pour l'apprentissage fédéré hétérogène. (Bidirectional compression for Federated Learning in a heterogeneous setting). - Constantin Philippenko (2023)

In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting. - Constantin Philippenko; Kevin Scaman; Laurent Massoulié (AAAI, 2025)

Adaptive collaboration for online personalized distributed learning with heterogeneous clients. - Constantin Philippenko; Batiste Le Bars; Kevin Scaman; Laurent Massoulié (CoRR, 2025)