Federated learning with incremental clustering for heterogeneous data

no code implementations • 17 Jun 2022 • Fabiola Espinoza Castellon, Aurelien Mayoue, Jacques-Henri Sublemontier, Cedric Gouy-Pailler

To prevent such a bottleneck, we propose FLIC (Federated Learning with Incremental Clustering), in which the server exploits the updates sent by clients during federated training instead of asking them to send their parameters simultaneously.

Clustering Federated Learning

Structured adaptive and random spinners for fast machine learning computations

no code implementations • 19 Oct 2016 • Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Francois Fagan, Cedric Gouy-Pailler, Anne Morvan, Nourhan Sakr, Tamas Sarlos, Jamal Atif

We consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy.

BIG-bench Machine Learning Dimensionality Reduction +1

TripleSpin - a generic compact paradigm for fast machine learning computations

no code implementations • 29 May 2016 • Krzysztof Choromanski, Francois Fagan, Cedric Gouy-Pailler, Anne Morvan, Tamas Sarlos, Jamal Atif

In particular, as a byproduct of the presented techniques and by using relatively new Berry-Esseen-type CLT for random vectors, we give the first theoretical guarantees for one of the most efficient existing LSH algorithms based on the $\textbf{HD}_{3}\textbf{HD}_{2}\textbf{HD}_{1}$ structured matrix ("Practical and Optimal LSH for Angular Distance").

BIG-bench Machine Learning Quantization