Federated Machine Learning Solutions: A Systematic Review
Abstract
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.
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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