Federated Learning for Cyber Physical Systems: A Comprehensive Survey

TL;DR

This survey comprehensively reviews recent advances in federated learning applied to cyber physical systems, highlighting applications, system architectures, algorithms, and future research directions in this rapidly evolving field.

Contribution

It provides an extensive analysis of the integration of federated learning with cyber physical systems, covering recent developments, applications, and critical insights for future research.

Findings

Federated learning enhances data privacy in CPS applications.

FL-CPS integration improves decision-making in smart cities and healthcare.

Challenges include system heterogeneity and real-time constraints.

Abstract

The integration of machine learning (ML) in cyber physical systems (CPS) is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliability, device heterogeneity, and data privacy. There are also open research questions that must be addressed in order to fully realize the potential of ML in CPS. Federated learning (FL), a distributed approach to ML, has become increasingly popular in recent years. It allows models to be trained using data from decentralized sources. This approach has been gaining popularity in the CPS field, as it integrates computer, communication, and physical processes. Therefore, the purpose of this work is to provide a comprehensive analysis of the most recent developments of FL-CPS, including the numerous application areas, system topologies, and algorithms developed in recent years. The paper starts by discussing recent advances in both FL and CPS, followed by their integration. Then, the paper compares the application of FL in CPS with its applications in the internet of things (IoT) in further depth to show their connections and distinctions. Furthermore, the article scrutinizes how FL is utilized in critical CPS applications, e.g., intelligent transportation systems, cybersecurity services, smart cities, and smart healthcare solutions. The study also includes critical insights and lessons learned from various FL-CPS implementations. The paper's concluding section delves into significant concerns and suggests avenues for further research in this fast-paced and dynamic era.