SoK: Assessing the State of Applied Federated Machine Learning
It addresses the gap between theoretical promise and real-world implementation of FedML for privacy-critical applications, but is incremental as it synthesizes existing research rather than proposing new methods.
This study conducted a systematic literature review of 74 papers to assess the current state of applied Federated Machine Learning (FedML), identifying challenges that hinder its practical adoption in privacy-critical domains despite its theoretical benefits for data privacy.
Machine Learning (ML) has shown significant potential in various applications; however, its adoption in privacy-critical domains has been limited due to concerns about data privacy. A promising solution to this issue is Federated Machine Learning (FedML), a model-to-data approach that prioritizes data privacy. By enabling ML algorithms to be applied directly to distributed data sources without sharing raw data, FedML offers enhanced privacy protections, making it suitable for privacy-critical environments. Despite its theoretical benefits, FedML has not seen widespread practical implementation. This study aims to explore the current state of applied FedML and identify the challenges hindering its practical adoption. Through a comprehensive systematic literature review, we assess 74 relevant papers to analyze the real-world applicability of FedML. Our analysis focuses on the characteristics and emerging trends of FedML implementations, as well as the motivational drivers and application domains. We also discuss the encountered challenges in integrating FedML into real-life settings. By shedding light on the existing landscape and potential obstacles, this research contributes to the further development and implementation of FedML in privacy-critical scenarios.