Search Results for author:
Found 5 papers, 4 papers with code
In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting
We provide a linear rate of convergence of the excess loss which depends on $\sigma_{\max} / \sigma_{r}$, where $\sigma_{r}$ is the $r^{\mathrm{th}}$ singular value of the concatenation $\mathbf{S}$ of the matrices $(\mathbf{S}^i)_{i=1}^N$.
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning
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.
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
Preserved central model for faster bidirectional compression in distributed settings
To obtain this improvement, we design MCM, an algorithm such that the downlink compression only impacts local models, while the global model is preserved.
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees
We introduce a framework - Artemis - to tackle the problem of learning in a distributed or federated setting with communication constraints and device partial participation.
