Fantastyc: Blockchain-based Federated Learning Made Secure and Practical

Autor: Boitier, William, Del Pozzo, Antonella, García-Pérez, Álvaro, Gazut, Stephane, Jobic, Pierre, Lemaire, Alexis, Mahe, Erwan, Mayoue, Aurelien, Perion, Maxence, Rezende, Tuanir Franca, Singh, Deepika, Tucci-Piergiovanni, Sara

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 sharing their local data. The centrality of this framework represents

Externí odkaz: http://arxiv.org/abs/2406.03608

dnadna: DEEP NEURAL ARCHITECTURES FOR DNA - A DEEP LEARNING FRAMEWORK FOR POPULATION GENETIC INFERENCE

Autor: Sanchez, Théophile, Madison Bray, Erik, Jobic, Pierre, Guez, Jérémy, Letournel, Anne-Catherine, Charpiat, Guillaume, Cury, Jean, Jay, Flora

We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination, and reusability of neural networks designed for gene

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ba9b401a8306a332cc3d8abf4baff777
https://hal.archives-ouvertes.fr/hal-03352910v2/document

dnadna: a deep learning framework for population genetics inference.

Autor: Sanchez T; Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France., Bray EM; Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France., Jobic P; Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France.; ENS Paris-Saclay, 91190 Gif-sur-Yvette, France., Guez J; Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France.; UMR7206 Eco-Anthropologie, Muséum National d'Histoire Naturelle, CNRS, Université de Paris, 75016 Paris, France., Letournel AC; Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France., Charpiat G; Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France., Cury J; Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France.; SEED, U1284, INSERM, Université de Paris, 75004 Paris, France., Jay F; Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France.

Publikováno v: Bioinformatics (Oxford, England) [Bioinformatics] 2023 Jan 01; Vol. 39 (1).