TY - GEN
T1 - Supporting Managerial Decision-Making for Federated Machine Learning
T2 - 57th Annual Hawaii International Conference on System Sciences, HICSS 2024
AU - Zahn, Milena
AU - Müller, Tobias
AU - Matthes, Florian
N1 - Publisher Copyright:
© 2024 IEEE Computer Society. All rights reserved.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Design Science Research
KW - Federated Machine Learning
KW - Technology Adoption
UR - http://www.scopus.com/inward/record.url?scp=85199768240&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85199768240
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 6738
EP - 6747
BT - Proceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024
A2 - Bui, Tung X.
PB - IEEE Computer Society
Y2 - 3 January 2024 through 6 January 2024
ER -