Machine learning for all : a methodology for choosing a federated learning approach
Abstract
Federated Learning is a new form of Machine Learning where a central model is trained decentrally on multiple distributed devices, while still keeping data on-device for privacy-preservation. Organizations who want to tap into the potential of having more data available for their predictive machine learning models, while still adhering to recent data protection regulations, will see a good fit in Federated Learning, as privacy-preservation is one of its main pillars. However, the research area is relatively new and the information fragmented. Therefore, this study provides a comprehensive review on the state-of-the-art in Federated Learning research. It sets an agreed-upon definition for Federated Learning, presents a comprehensive list of available Federated Learning algorithms, and purposefully investigates their main differences. All this is consolidated and used to design a methodology that supports organizations in making an informed decision in choosing among the myriad of Federated Learning algorithms available, based on their data-related characteristics, privacy-requirements, and business goals. This method has been successfully evaluated in three ways, to show the practicality and validate the choice based on empirical results, not just on literature insights, giving it both scientific backing and practical relevance.
How strong is the evidence?
Evidence Hub reads this as a thesis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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