Collaboration Management for Federated Learning

Marius Schlegel, Daniel Scheliga, Kai-Uwe Sattler, Marco Seeland, Patrick Mäder

Abstract: Federated learning (FL) enables collaborative and privacy-preserving training of machine learning (ML) models on federated data. However, the barriers to using FL are still high. First, collaboration procedures are use-case-specific and require manual preparation, setup, and configuration of execution environments. Second, establishing collaborations and matching collaborators is time-consuming due to heterogeneous intents as well as data properties and distributions. Third, debugging the process and keeping track of the artifacts created and used during collaboration is challenging. Our goal is to reduce these barriers by requiring as little technical knowledge from collaborators as possible. We contribute mechanisms for flexible collaboration composition and creation, automated collaborator matching, and provenance-based collaboration and artifact management.