Guiding the Practical Adoption of Federated Machine Learning in Organizations

Federated Machine Learning enables multiple organizations to jointly train a Machine Learning model without direct data sharing. Despite this potential, most projects do not lead to a productive use of the technology. This dissertation investigates this challenge and identifies the influencing factors for successful practical adoption as well as the socio-technical aspects of collaborations. Moreover, this thesis develops a process model for the software development life cycle.