A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective

Federated learning is an emerging machine learning paradigm where multiple 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 from a software engineering perspective, we performed a systematic literature review with the extracted 231 primary studies. The results show that most of the known motivations of federated learning appear to be the most studied federated learning challenges, such as communication efficiency and statistical heterogeneity. Also, there are only a few real-world applications of federated learning. Hence, more studies in this area are needed before the actual industrial-level adoption of federated learning.