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A Quorum-Based Privacy-Preserving Distributed Learning Framework for Anomaly Detection

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
P.S.S. Pranav
Parth Nagar
Ankit Kumar Singh
M. S. Srinath
Published in
Security and Privacy
Publisher
Springer Nature Switzerland

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Abstract

This chapter introduces a quorum-based privacy-preserving distributed learning framework designed for anomaly detection. The framework leverages distributed learning techniques to enhance model performance while ensuring data privacy through a quorum consensus protocol. The text explores the importance of data sharing in improving model accuracy and generalization, highlighting the challenges of privacy risks associated with data sharing. It presents a detailed methodology for implementing the framework, including the definition of local and global anomalies, and the use of isolation forests for anomaly detection. The framework is evaluated using the Credit Card Fraud Detection dataset, demonstrating its effectiveness in improving anomaly detection performance compared to local models. The chapter also discusses the impact of different quorum values and the benefits of weighted quorum-based global models. The results show that the proposed framework consistently outperforms single-client detection across various client setups, making it a robust solution for privacy-preserving anomaly detection in distributed environments.
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previous chapter A Lightweight Intrusion Detection Framework for IoT Using Fisher Score Feature Filtering and ML Models
next chapter Hybrid DCGAN-ResNet50 Model for Fake Face Detection
Title
A Quorum-Based Privacy-Preserving Distributed Learning Framework for Anomaly Detection
Authors
P.S.S. Pranav
Parth Nagar
Ankit Kumar Singh
M. S. Srinath
Copyright Year
2026
Book
Security and Privacy

Print ISBN: 978-3-032-12833-1

Electronic ISBN: 978-3-032-12834-8

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-12834-8_12
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