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Privacy-Preserving and Secure Federated Learning for IoT, Cyber-Physical, and Maritime Systems

Submission Deadline: 31 July 2026 View: 978 Submit to Special Issue

Guest Editors

Dr. Zhen-Yu Wu

Email: hkwu668@nkust.edu.tw

Affiliation: Department of Maritime Information and Technology, National Kaohsiung University of Science and Technology, Kaohsiung, 807618, Taiwan

Homepage:

Research Interests: maritime IoT applications, information security, post-quantum cryptography, federated learning, e-health systems, and AI-based data analysis

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Summary

As Internet of Things (IoT) and cyber-physical systems (CPS) rapidly expand across smart cities, healthcare, transportation, and maritime domains, the demand for secure collaboration and trustworthy intelligence becomes increasingly critical. Federated learning enables distributed AI model training without sharing raw data, but emerging threats—including quantum-era attacks and data leakage—require advanced privacy-preserving mechanisms.


This Special Issue invites original research and surveys on secure, privacy-aware, and AI-driven architectures for next-generation IoT and CPS ecosystems. Topics of interest include federated learning algorithms, post-quantum cryptography, blockchain-based trust management, secure edge-cloud orchestration, and intelligent intrusion detection. By integrating security, resilience, and decentralized intelligence, this issue aims to advance dependable distributed learning frameworks that support sustainable IoT applications across industrial, urban, healthcare, and maritime environments.


Suggested Themes:
· Federated learning and privacy-preserving AI in distributed environments
· Quantum-resistant cryptography and post-quantum security for IoT/CPS
· Blockchain and decentralized trust management mechanisms
· Secure edge-cloud orchestration for intelligent IoT systems
· Intrusion detection and anomaly analysis in autonomous networks
· Lightweight and energy-efficient security protocols
· Secure data aggregation and integrity assurance across heterogeneous domains


Keywords

federated learning, IoT, cyber-physical systems, privacy-preserving AI, blockchain, quantum-safe security, edge intelligence, secure data aggregation, trust management

Published Papers


  • Open Access

    ARTICLE

    SubPFed: A Personalized Federated Learning Approach with Subgraphs

    Jianbin Li, Hang Bao, Xin Tong
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076676
    (This article belongs to the Special Issue: Privacy-Preserving and Secure Federated Learning for IoT, Cyber-Physical, and Maritime Systems)
    Abstract The proliferation of large-scale graph data has enabled Graph Neural Networks (GNNs) to achieve significant success in domains such as recommender systems, social network analysis, and biomedicine. However, in practical networked environments, particularly in distributed service infrastructures, graph data is often isolated between multiple edge smart devices and cannot be shared due to privacy, making GNN models weak in generalization. Subgraph Federated Learning (SFL) mitigates this challenge by treating local client data as subgraphs of the global graph to decentralized GNN training. Unfortunately, client-side missing edges make GNN model difficult to capture dependency information between… More >

  • Open Access

    ARTICLE

    FedGLP-ADP: Federated Learning with Gradient-Based Layer-Wise Personalization and Adaptive Differential Privacy

    Di Xiao, Wenting Jiang, Min Li
    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079808
    (This article belongs to the Special Issue: Privacy-Preserving and Secure Federated Learning for IoT, Cyber-Physical, and Maritime Systems)
    Abstract The rapid advancement of the Internet of Things (IoT) has transformed edge devices from simple data collectors into intelligent units capable of local processing and collaborative learning. However, the vast amounts of sensitive data generated by these devices face severe constraints from “data silos” and risks of privacy breaches. Federated learning (FL), as a distributed collaborative paradigm that avoids sharing raw data, holds great promise in the IoT domain. Nevertheless, it remains vulnerable to gradient leakage threats. While traditional differential privacy (DP) techniques mitigate privacy risks, they often come at the cost of significantly reduced… More >

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