AI Security and Edge Computing in Distributed Edge Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 271

Special Issue Editors


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Guest Editor
Institutes of Artificial Intelligence, Guangzhou University, Guangzhou 510006, China
Interests: machine learning security; Internet of Things; edge computing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou 510006, China
Interests: machine learning; privacy preserving

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Guest Editor Assistant
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Interests: AI security; deep learning; distributed learning

Special Issue Information

Dear Colleagues,

The advent of edge computing has revolutionized the way we process and analyze data, bringing computational power closer to the data source. This paradigm shift has enabled numerous applications across various domains, from smart cities to industrial automation. With the integration of Artificial Intelligence (AI) techniques into edge systems, the potential for innovation and efficiency gains is vast. However, this integration also introduces significant security challenges that must be addressed to ensure the robustness and reliability of these systems. This Special Issue aims to provide a platform for researchers and practitioners to disseminate their latest findings, exchange ideas, and discuss challenges and solutions related to the security aspects of AI in distributed edge environments.

Topics of interest include, but are not limited to, the following:

  • Security threats and vulnerabilities in AI-enabled edge systems;
  • Privacy-preserving AI techniques for edge computing;
  • Secure and efficient distributed machine learning algorithms for edge devices;
  • Trustworthiness and integrity of AI models deployed at the edge;
  • Secure communication protocols for edge-to-cloud and edge-to-edge interactions;
  • Threat modeling and risk assessment in edge computing environments;
  • Intrusion detection and prevention systems for edge networks;
  • Hardware-based security solutions for edge devices;
  • Federated learning and secure aggregation in edge computing;
  • Regulatory and ethical considerations in securing AI at the edge.

Dr. Kongyang Chen
Dr. Hongyang Yan
Guest Editors

Dr. Yatie Xiao
Guest Editor Assistant

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Keywords

  • edge computing
  • artificial intelligence
  • AI security
  • distributed systems
  • privacy preservation
  • machine learning
  • distributed learning
  • federated learning
  • edge-to-cloud communication
  • large-language model

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