Abstract
Federated Learning (FL) is a new paradigm aiming to solve 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 solution by moving the focus from sharing data to sharing
models. The FL paradigm involves different entities (institutions)
holding proprietary datasets, contributing with each other to train a
global Artificial Intelligence (AI) model using their own locally
available data. Although several studies propose ways to distribute the
computation or aggregate results, fewer efforts have been made on how to
implement it. With the ambition of helping accelerate 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 the assessment of
the growing trend of the tool’s popularity over a six months time
window. Finally, a ranking of the maturity of the tools is derived based
on keyaspects to consider when building an FL pipeline.