SoK: Assessing the State of Applied Federated Machine Learning
Tobias Müller Maximilian Stäbler Hugo Gascón Frank Köster Florian Matthes
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.