FeLebrities: A User-Centric Assessment of Federated Learning Frameworks

Abstract

Federated Learning (FL) is a new paradigm aimed at solving data access problems. It provides a solution by moving the focus from sharing data to sharing models. The FL paradigm involves different entities (institutions) holding proprietary datasets that, contributing with each other to train a global Artificial Intelligence (AI) model using their own locally available data. Although several studies have proposed methods to distribute the computation or aggregate results, few efforts have been made to cover on how to implement FL pipelines. With the aim of accelerating the exploitation of FL frameworks, this paper proposes a survey of public tools that are currently available for building FL pipelines, an objective ranking based on the current state of user preferences, and an assessment of the growing trend of the tool's popularity over a one year time window, with measurements performed every six months. These measurements include objective metrics, like the number of 'Watch,' 'Star' and 'Follow' available from software repositories as well as thirteen custom metrics grouped into three main categories: Usability, Portability, and Flexibility. Finally, a ranking of the maturity of the tools is derived based on the key aspects to consider when building a FL pipeline.