3 papers
cs.LG2025
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning
Constantin Philippenko, Aymeric Dieuleveut
In this paper, we investigate the impact of compression on stochastic gradient algorithms for machine learning, a technique widely used in distributed and federated learning. We un…
cs.LG2025
In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting
Constantin Philippenko, Kevin Scaman, Laurent Massoulié
We analyze a distributed algorithm to compute a low-rank matrix factorization on clients, each holding a local dataset , mathematica…
stat.ML2025
Adaptive collaboration for online personalized distributed learning with heterogeneous clients
Constantin Philippenko, Batiste Le Bars, Kevin Scaman +1
We study the problem of online personalized decentralized learning with statistically heterogeneous clients collaborating to accelerate local training. An important challenge i…