On the Adoption of Federated Machine Learning: Roles, Activities and Process Life Cycle

Tobias Müller, Tobias Müller, Milena Zahn, Milena Zahn, Florian Matthes

2023

Abstract

Federated Machine Learning is a promising approach for training machine learning models on decentralized data without the need for data centralization. Through a model-to-data approach, Federated Machine Learning yields huge potential from privacy by design to heavily reducing communication costs and offline usage. However, the implementation and management of Federated Machine Learning projects can be challenging, as it involves coordinating multiple parties across different stages of the project life cycle. We observed that Federated Machine Learning is missing clarity over the individual involved roles including their activities, interactions, dependencies, and responsibilities which are needed to establish governance and help practitioners operationalize Federated Machine Learning projects. We argue that a process model, which is closely aligned with established MLOps principles can provide this clarification. In this position paper, we make a case for the necessity of a role model to structure distinct roles, an activity model to understand the involvement and operations of each role, and an artifact model to demonstrate how artifacts are used and structured. Additionally, we argue, that a process model is needed to capture the dependencies and interactions between the roles, activities, and artifacts across the different stages of the life cycle. Furthermore, we describe our research approach and the current status of our ongoing research toward this goal. We believe that our proposed process model will provide a foundation for the governance of Federated Machine Learning projects, and enable practitioners to leverage the benefits of decentralized data computation.

Paper Citation

in Harvard Style

Müller T., Zahn M. and Matthes F. (2023). On the Adoption of Federated Machine Learning: Roles, Activities and Process Life Cycle. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-648-4, SciTePress, pages 525-531. DOI: 10.5220/0011954500003467

in Bibtex Style

@conference{iceis23,
author={Tobias Müller and Milena Zahn and Florian Matthes},
title={On the Adoption of Federated Machine Learning: Roles, Activities and Process Life Cycle},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2023},
pages={525-531},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011954500003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - On the Adoption of Federated Machine Learning: Roles, Activities and Process Life Cycle
SN - 978-989-758-648-4
AU - Müller T.
AU - Zahn M.
AU - Matthes F.
PY - 2023
SP - 525
EP - 531
DO - 10.5220/0011954500003467
PB - SciTePress