Bridging Research and Practice in Federated Learning: A Multivocal Review of System-Level Architectures and Processes

Paper in proceeding, 2027

Federated Learning (FL) was introduced as a decentralized machine learning approach that enables models to be trained on distributed data sources without exposing raw data. Over time, it became clear that FL as an architectural paradigm can become a tipping point in transforming software-intensive systems into dynamic and autonomous intelligence. However, the adoption of FL in large-scale software-intensive system has been slow. This means there is a growing gap between what is being studied in research and what industry actually needs. This paper presents a multivocal literature review (MLR) that integrates evidence from systematic literature reviews, primary studies, and industrial grey literature. The study examines FL from architectural and process perspectives, focusing on how systems are designed, deployed, and operated across research and practice. The results show that, first, significant progress has been made in algorithm design and optimization, while system-level challenges, such as orchestration and lifecycle management, remain understudied. Second, academic work is mostly challenge-driven and focuses on solving problems in isolation, such as addressing system heterogeneity and communication cost, while industry emphasizes system design and operational constraints. Lastly, deployment, evaluation, and end-to-end integration remain insufficiently addressed in both domains.