{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T16:46:43Z","timestamp":1762361203651,"version":"build-2065373602"},"reference-count":93,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Northwestern Mutual Data Science Institute"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Software"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/software4040028","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T16:09:47Z","timestamp":1762358987000},"page":"28","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Software Engineering Aspects of Federated Learning Libraries: A Comparative Survey"],"prefix":"10.3390","volume":"4","author":[{"given":"Hiba","family":"Alsghaier","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Wisconsin\u2013Milwaukee, Milwaukee, WI 53211, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6456-9763","authenticated-orcid":false,"given":"Tian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Wisconsin\u2013Milwaukee, Milwaukee, WI 53211, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3347","DOI":"10.1109\/TKDE.2021.3124599","article-title":"A survey on federated learning systems: Vision, hype and reality for data privacy and protection","volume":"35","author":"Li","year":"2023","journal-title":"IEEE Trans. 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