39 citations · 51 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
Preserved central model for faster bidirectional compression in distributed settings
Constantin Philippenko, Aymeric Dieuleveut
We develop a new approach to tackle communication constraints in a distributed learning problem with a central server. We propose and analyze a new algorithm that performs bidirect…
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees
Constantin Philippenko, Aymeric Dieuleveut
We introduce a framework - Artemis - to tackle the problem of learning in a distributed or federated setting with communication constraints and device partial participation. Severa…