1 citations · 1 across the 3 of their papers we have counts for
3 papers
Fantastyc: Blockchain-based Federated Learning Made Secure and Practical
William Boitier, Antonella Del Pozzo, Álvaro García-Pérez +9
Federated Learning is a decentralized framework that enables multiple clients to collaboratively train a machine learning model under the orchestration of a central server without…
Dataset Dictionary Learning in a Wasserstein Space for Federated Domain Adaptation
Eduardo Fernandes Montesuma, Fabiola Espinoza Castellon, Fred Ngolè Mboula +3
Multi-Source Domain Adaptation (MSDA) is a challenging scenario where multiple related and heterogeneous source datasets must be adapted to an unlabeled target dataset. Conventiona…
Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
Fabiola Espinoza Castellon, Eduardo Fernandes Montesuma, Fred Ngolè Mboula +3
In this article, we propose an approach for federated domain adaptation, a setting where distributional shift exists among clients and some have unlabeled data. The proposed framew…