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

TL;DR

This paper systematically reviews federated machine learning from a software engineering perspective, analyzing 231 studies to understand its development lifecycle and identify future research directions.

Contribution

It provides a comprehensive synthesis of federated learning development stages and highlights future trends from a software engineering viewpoint.

Findings

Summarizes current state-of-the-art in federated learning

Identifies gaps and challenges in federated system development

Suggests future research directions

Abstract

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.