Software Engineering Aspects of Federated Learning Libraries: A Comparative Survey

Federated Learning (FL) has emerged as a pivotal paradigm for privacy-preserving machine learning. While numerous FL libraries have been developed to operationalize this paradigm, their rapid proliferation has created a significant challenge for practitioners and researchers: selecting the right tool requires a deep understanding of their often undocumented software architectures and extensibility, aspects that are largely overlooked by existing algorithm-focused surveys. This paper addresses this gap by conducting the first comprehensive survey of FL libraries from a software engineering perspective. We systematically analyze ten popular open-source FL libraries, dissecting their architectural designs, support for core and advanced FL features, and most importantly, their extension mechanisms for customization. Our analysis produces a novel taxonomy of FL concepts grounded in software implementation, a practical decision framework for library selection, and an in-depth discussion of architectural limitations and pathways for future development. The findings provide developers with actionable guidance for selecting and extending FL tools and offer researchers a clear roadmap for advancing FL infrastructure.