Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
It addresses adoption problems for organizations under data regulations, but is incremental as it identifies gaps without proposing solutions.
The paper investigates practical challenges hindering cross-silo federated learning adoption through interviews with stakeholders, revealing barriers like model performance concerns and trust issues distinct from other FL forms.
Cross-silo federated learning (FL) is a promising approach to enable cross-organization collaboration in machine learning model development without directly sharing private data. Despite growing organizational interest driven by data protection regulations such as GDPR and HIPAA, the adoption of cross-silo FL remains limited in practice. In this paper, we conduct an interview study to understand the practical challenges associated with cross-silo FL adoption. With interviews spanning a diverse set of stakeholders such as user organizations, software providers, and academic researchers, we uncover various barriers, from concerns about model performance to questions of incentives and trust between participating organizations. Our study shows that cross-silo FL faces a set of challenges that have yet to be well-captured by existing research in the area and are quite distinct from other forms of federated learning such as cross-device FL. We end with a discussion on future research directions that can help overcome these challenges.