@misc{10481/77990,
year = {2020},
month = {10},
url = {https://hdl.handle.net/10481/77990},
abstract = {The high demand of artificial intelligence services at the edges that also preserve data privacy
has pushed the research on novel machine learning paradigms that fit these requirements.
Federated learning has the ambition to protect data privacy through distributed
learning methods that keep the data in its storage silos. Likewise, differential privacy
attains to improve the protection of data privacy by measuring the privacy loss in the
communication among the elements of federated learning. The prospective matching of
federated learning and differential privacy to the challenges of data privacy protection
has caused the release of several software tools that support their functionalities, but they
lack a unified vision of these techniques, and a methodological workflow that supports
their usage. Hence, we present the Sherpa.ai Federated Learning framework that is
built upon a holistic view of federated learning and differential privacy. It results from
both the study of how to adapt the machine learning paradigm to federated learning,
and the definition of methodological guidelines for developing artificial intelligence services
based on federated learning and differential privacy. We show how to follow the
methodological guidelines with the Sherpa.ai Federated Learning framework by means
of a classification and a regression use cases.},
organization = {SHERPA Europe S.L.	OTRI-4137},
organization = {Spanish Government},
organization = {European Commission	TIN2017-89517-P},
organization = {Spanish Government fellowship programmes Formacion de Profesorado Universitario	FPU18/04475
Juan de la Cierva Incorporacion	IJC2018-036092-I},
publisher = {Elsevier},
keywords = {Federated learning},
keywords = {Differential privacy},
keywords = {Software framework},
keywords = {Sherpa.ai Federated Learning framework},
keywords = {Inteligencia artificial},
keywords = {Artificial intelligence},
title = {Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy},
author = {Rodríguez Barroso, Nuria and Jiménez López, Daniel and Ruiz Millán, José Antonio and Martínez Cámara, Eugenio and Luzón García, María Victoria and Herrera Triguero, Francisco},
}