Fedstellar: A Platform for Decentralized Federated Learning

arXiv:2306.09750v4 Announce Type: replace
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