TY - GEN
T1 - A Process Model for the Practical Adoption of Federated Machine Learning
AU - Müller, Tobias
AU - Zahn, Milena
AU - Matthes, Florian
N1 - Publisher Copyright:
© 2023 29th Annual Americas Conference on Information Systems, AMCIS 2023. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Applied AI
KW - Design Science Research
KW - Federated Machine Learning
KW - Process Model
KW - Software Engineering
UR - http://www.scopus.com/inward/record.url?scp=85192935580&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85192935580
T3 - 29th Annual Americas Conference on Information Systems, AMCIS 2023
BT - 29th Annual Americas Conference on Information Systems, AMCIS 2023
PB - Association for Information Systems
T2 - 29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023
Y2 - 10 August 2023 through 12 August 2023
ER -