FeLebrities: a user-centric assessment of Federated Learning frameworks

Abstract

<p>Federated Learning (FL) is a new paradigm that aims at solving the data access problem. It is gaining an increasing interest in a variety of research fields, including the Biomedical and Financial environments, where lots of valuable data sources are available but not often directly accessible due to the regulations that protect sensitive information. FL provides a wayout enabling the processing and sharing of data modeling solutions moving the focus from data to models. The FL paradigm involves different entities (institutions) holding proprietary datasets, contributing with each other to locally train a copy of a shared Artificial Intelligence (AI) model. Although there are different studies in the literature that suggest how to conceptually implement and orchestrate a federation, fewer efforts have been made on practical implications. With the ambition of helping accelerating the exploitation of FL frameworks, this paper proposes a survey of public tools that are currently available, an objective ranking based on current state of user preferences and the assessment of the growth trend of the tool popularity over a six months time window. Finally, a ranking of the tools maturity is derived based on key aspects to consider when building a FL pipeline.</p>