Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results, and identify future trends to encourage researchers to advance their current work.
@article{arxiv.2007.11354,
title = {A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective},
author = {Sin Kit Lo and Qinghua Lu and Chen Wang and Hye-Young Paik and Liming Zhu},
journal= {arXiv preprint arXiv:2007.11354},
year = {2021}
}
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Published on ACM Computing Survey. Latest version available here: https://dl.acm.org/doi/10.1145/3450288