Abstract
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.