Federated Machine Learning Solutions: A Systematic Review

Federated machine learning (FL) provides a privacy-preserving alternative to centralized machine learning by enabling collaborative model training without data sharing, yet its cross-domain implementation faces underexplored benefits and challenges. This study addresses these gaps and benefits by a systematic review of FL solutions, analyzing domain-specific applications, key benefits, critical challenges, and domain-specific trade-offs considered in the implementation of FL. Our contributions include a structured comparison of FL adoption across domains and actionable research directions to improve scalability, efficiency, and real-world deployment. By consolidating these insights, we offer future directions for advancing FL’s adoption while addressing its current limitations.