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Fedstellar: A Platform for Decentralized Federated Learning
April 9, 2024, 4:43 a.m. | Enrique Tom\'as Mart\'inez Beltr\'an, \'Angel Luis Perales G\'omez, Chao Feng, Pedro Miguel S\'anchez S\'anchez, Sergio L\'opez Bernal, G\'er\^ome Bov
cs.LG updates on arXiv.org arxiv.org
Abstract: In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train Machine Learning (ML) models across the participants of a federation while preserving data privacy. Since its birth, Centralized FL (CFL) has been the most used approach, where a central entity aggregates participants' models to create a global one. However, CFL presents limitations such as communication bottlenecks, single point of failure, and reliance on a central server. Decentralized Federated Learning (DFL) addresses these …
abstract arxiv birth cs.ai cs.dc cs.lg cs.ni data data privacy decentralized federated learning federation google machine machine learning novel paradigm platform privacy train type
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