A Process Model for the Practical Adoption of Federated Machine Learning

Tobias Müller, Milena Zahn, Florian Matthes

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

The wealth of digitized data forms the fundamental basis for the disruptive impact of Machine Learning. Yet a significant amount of data is scattered and locked in data silos, leaving its full potential untouched. Federated Machine Learning is a novel Machine Learning paradigm with the ability to overcome data silos by enabling the training of Machine Learning models on decentralized, potentially siloed data. Despite its advantages, most Federated Machine Learning projects fail in the project initiation phase due to their decentralized structure and incomprehensive interrelations. The current literature lacks a comprehensible overview of the complex project structure. Through a Design Science Research approach, we provide a process model of a Federated Machine Learning life cycle including required activities, roles, resources, artifacts, and interrelations. Thereby, we aim to aid practitioners in the project initiation phase by providing transparency and facilitating comprehensibility over the entire project life cycle.

OriginalspracheEnglisch
Titel29th Annual Americas Conference on Information Systems, AMCIS 2023
Herausgeber (Verlag)Association for Information Systems
ISBN (elektronisch)9781713893592
PublikationsstatusVeröffentlicht - 2023
Veranstaltung29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023 - Panama City, Panama
Dauer: 10 Aug. 202312 Aug. 2023

Publikationsreihe

Name29th Annual Americas Conference on Information Systems, AMCIS 2023

Konferenz

Konferenz29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023
Land/GebietPanama
OrtPanama City
Zeitraum10/08/2312/08/23

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