Supporting Managerial Decision-Making for Federated Machine Learning: Design of a Technology Selection Tool

Abstract

The insufficient amount of training data is a persisting bottleneck of Machine Learning systems. A large portion of the world’s data is scattered and locked in data silos. Breaking up these data silos could alleviate this problem. Federated Machine Learning is a novel model-to-data approach that enables the training of Machine Learning models, on decentralized, potentially siloed data. Despite its promising potential, most Federated Machine Learning projects never leave the prototype stage. This can be attributed to exaggerated expectations and an inappropriate fit between the technology and the use case. Current literature does not offer guidance for assessing the fit between Federated Machine Learning and their use case. Against this backdrop, we design a decision-support tool to aid decision-makers in the suitability and complexity assessment of FedML projects. Thereby, we aim to facilitate the technology selection process, avoid exaggerated expectations and consequently facilitate the success of Federated Machine Learning projects.