Article Title: A Unified Framework for Enhancing Federated Learning Security and Robustness Using Generative Adversarial Networks Blockchain and Differential Privacy
Volume Number: 1
Issue: 1
Year: 2025
Article Type: Original Article
Author Names: Shehryar Qamar Paracha¹*, Muhammad Inam ul Haq¹, Rana Ali Sher Haide², Adil Khan³, Nageeta Kumari⁴, Hafiza Tahira Farzand⁵, Khaleeq Uddin⁶, and Habib Ur Rehman⁷
Page Number: 1-9
PDF: [Download]
DOI: https://doi.org/10.64368/ejeti.vol.1.issue1.1
Affiliations:
¹Department of Electronic Engineering, The Islamia University of Bahawalpur, Pakistan
²Department of Mathematics, COMSATS University, Pakistan
³Department of Computer Science, Abasyn University, Pakistan
⁴Department of Software Project Management, National University of Computer and Emerging Science (FAST-NUCES), Pakistan
⁵Department of Electronic Engineering, The Islamia University of Bahawalpur, Pakistan
⁶Department of Computer Science, University of the Punjab, Pakistan
⁷Department of Computer Science, University of Karachi, Pakistan
*Correspondence: Mr. Shehryar Qamar Paracha, Department of Electronic Engineering, The Islamia University of Bahawalpur, Pakistan, Email Address: shehryar.paracha@iub.edu.pk
Keywords: Federated Learning; Generative Adversarial Networks; Blockchain Technology; Differential Privacy; Adversarial Robustness; Secure Decentralized Systems
Abstract: To define the approach taken by federated learning AI models more distinctly, FL enables sharing the model across the range of networks without revealing sensitive data. The key problem, however, is that this model does not provide a centralized authority for data control and as such is prone to adversarial and data poisoning attacks. This problem leads to a compromise between the model accuracy and the model security. In solving these problems, we suggest a framework for federated learning GAN-augmented with blockchain and differential privacy. The GAN module is responsible for performing adversarial attacks on the AI model which gives enables any real-world model to be robust to true attacks. GANs also assist in detecting poisoned updates and as such enables security improvement. Blockchain guarantees trust and transparency between the participating clients and differential privacy provides confidential data collection for effective counter measures to privacy breaches. The effectiveness of the framework has been verified experimentally and it has been proven that the integrated framework successfully reduced the success rate of the attacks against it to 8.0%, increased the detection accuracy to 98.0% and maintained the model accuracy at the level of 95.0%. It is now evident that due to the integration of GANs, Blockchain and differential privacy FL is challenging to be combated against in a malicious manner without compromising the target model performance significantly. The exceptional detection accuracy ratio guarantees that any corrupted updates will be captured before doing so much damage that it alters the e-learning process. This necessary balance of model performance and trust with high detection rate enables new GAN, blockchain, and differential privacy model to be used in protecting the learning process. This novel capability will help alleviate privacy concerns in critical settings like healthcare, finance, and cybersecurity, where information is central to improving outcomes.
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