  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.
