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

Milena Zahn, Tobias Müller, Florian Matthes

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

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.

OriginalspracheEnglisch
TitelProceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024
Redakteure/-innenTung X. Bui
Herausgeber (Verlag)IEEE Computer Society
Seiten6738-6747
Seitenumfang10
ISBN (elektronisch)9780998133171
PublikationsstatusVeröffentlicht - 2024
Veranstaltung57th Annual Hawaii International Conference on System Sciences, HICSS 2024 - Honolulu, USA/Vereinigte Staaten
Dauer: 3 Jan. 20246 Jan. 2024

Publikationsreihe

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Konferenz

Konferenz57th Annual Hawaii International Conference on System Sciences, HICSS 2024
Land/GebietUSA/Vereinigte Staaten
OrtHonolulu
Zeitraum3/01/246/01/24

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