Federated Machine Learning Solutions: A Systematic Review

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Systematic review identifies benefits and challenges of federated machine learning, suggesting improvements for real-world deployment and efficiency.

Federated machine learning offers a privacy-preserving alternative to centralized methods, improving data security.
The analysis reveals key benefits such as collaboration without data sharing and challenges including scalability limitations.
Our review systematically compares domain-specific applications and trade-offs in federated machine learning implementation.
Addressing the current limitations of federated machine learning may enhance its real-world adoption and performance.

Samuel et al. (2025) studied this question.

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Systematic review identifies benefits and challenges of federated machine learning, suggesting improvements for real-world deployment and efficiency.