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• W4386472890 abstract "Federated Learning (FL) is a new paradigm aiming to solve the data access problem. It 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 FL pipelines.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 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 key aspects to consider when building a FL pipeline." @default.
• W4386472890 created "2023-09-07" @default.
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• W4386472890 date "2023-01-01" @default.
• W4386472890 modified "2023-09-30" @default.
• W4386472890 title "FeLebrities: a user-centric assessment of Federated Learning frameworks" @default.
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• W4386472890 doi "https://doi.org/10.1109/access.2023.3312579" @default.
• W4386472890 hasPublicationYear "2023" @default.
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