SoK: Assessing the State of Applied Federated Machine Learning

TL;DR

This paper systematically reviews the current state of applied Federated Machine Learning, highlighting its potential, challenges, and the gap between theoretical benefits and practical implementation in privacy-sensitive domains.

Contribution

It provides a comprehensive analysis of 74 papers to assess real-world applicability, emerging trends, and challenges in implementing FedML.

Findings

Limited practical adoption of FedML despite theoretical advantages

Identification of key challenges in real-world FedML deployment

Emerging trends and application domains for FedML

Abstract

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.