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 issues by enabling …

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 issues by enabling …