Federated Learning Solution Blueprints for Use Cases Surveyed in Austrian Industries

Federated Learning (FL) holds immense potential for transforming the industrial landscape by leveraging distributed data to solve Artificial Intelligence (AI) use cases with collectively trained models in a privacy-preserving way. In this regard, Industrial FL (IFL) arose as a collaborative approach for training AI models between multiple industry partners and devices without the need to share the actual training data. However, despite its promising prospects, the transition and successful implementation of FL in practice is currently lagging behind, posing challenges for industrial companies. To address this, it is of crucial relevance to analyze different business types and involved stakeholders to be able to design FL-based solutions tailored to the industries needs. This paper presents the results of 13 semi-structured interviews conducted in Austrian industries, involving 11 companies from different domains. We identify AI applications, pain points, and attitudes towards AI and FL. Based on the interviews, three industry personas are derived, namely, service business, production optimization, and complex product and project business. To address the needs of these personas, three collaborative FL solution blueprints are proposed. The blueprints include system architectures, implementation steps, and collaboration modes for the involved parties. The blueprints are discussed based on dimensions such as FL paradigm, collaboration mode, key benefits, main addressed needs, and challenges.