mlsanityData to 7 Oct 2026

Federated Learning for Cyber Physical Systems: A Comprehensive Survey

arXiv 2505.04873 ↗First posted 8 May 2025cs.LG cs.AI cs.CRcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 96 of its 252 references that are in the map. First posted on arXiv 8 May 2025.

Builds on

96 of 252 references.

References · 96

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38 more
  1. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
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50 most cited of 96 All 96 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Datacited by 0
  2. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  3. 2026PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoTcited by 0
  4. 2026VARS-FL: Validation-Aligned Client Selection for Non-IID Federated Learning in IoT Systemscited by 0
  5. 2026StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splittingcited by 0
  6. 2026FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuningcited by 0