International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 17, No. 6, 8 Dec. 2025
Cover page and Table of Contents: PDF (size: 1424KB)
Innovative Privacy Preserving Strategies in Federal Learning
PDF (1424KB), PP.112-133
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Author(s)
Deny P. Francis
1,*
R. Sharmila
2
Index Terms
Federated Learning, Privacy-Preservation, Optimal Differential Privacy, DFT, AGC, Gradient Management
Abstract
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, but it remains vulnerable to privacy risks. This study introduces FL-ODP-DFT, a novel framework that integrates Optimal Differential Privacy (ODP) with Discrete Fourier Transform (DFT) to enhance both model performance and privacy. By transforming local gradients into the frequency domain, the method reduces data size and adds a layer of encryption before transmission. Adaptive Gaussian Clipping (AGC) is employed to dynamically adjust clipping thresholds based on gradient distribution, further improving gradient handling. ODP then calibrates noise addition based on data sensitivity and privacy budgets, ensuring a balance between privacy and accuracy. Extensive experiments demonstrate that FL-ODP-DFT outperforms existing techniques in terms of accuracy, computational efficiency, convergence speed, and privacy protection, making it a robust and scalable solution for privacy-preserving FL.
Cite This Paper
Deny P. Francis, R. Sharmila, "Innovative Privacy Preserving Strategies in Federal Learning", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.17, No.6, pp. 112-133, 2025. DOI:10.5815/ijieeb.2025.06.09
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