FeLebrities: a user-centric assessment of Federated Learning frameworks

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.