no code implementations • 14 Sep 2023 • Fabiola Espinoza Castellon, Eduardo Fernandes Montesuma, Fred Ngolè Mboula, Aurélien Mayoue, Antoine Souloumiac, Cédric Gouy-Pailler
The proposed framework, FedDaDiL, tackles the resulting challenge through dictionary learning of empirical distributions.
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.