Training Policy for Privacy-Preserving Logistic Regression in Federated Learning Environments

Cortés-Mendoza, Jorge M., Tchemykh, Andrei and González-Vélez, Horacio (2024) Training Policy for Privacy-Preserving Logistic Regression in Federated Learning Environments. In: 2024 2nd International Conference on Federated Learning Technologies and Applications (FLTA). IEEE, Valencia, Spain, pp. 1-8.

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Abstract

Logistic Regression (LR) is a widely used statistical model for classification problems. However, its training and evaluation in a shared environment increase the possibility of information leaking. A federated LR reduces security issues by using only locally available data for training. In a Federated Learning (FL) environment, LR receives the coefficients of local models to create the federated LR model, which is then distributed to update the local models. The exchange process does not leak confidential information when LR coefficients are encrypted. Homomorphic Encryption (HE) allows the merging of local LR models with privacy preservation (HE-LR). This work presents a novel training policy to reduce the training time with only slightly decreased quality in an FL environment with HE. We analyze the accuracy and time of FL policies with HE-LR that progressively reduce the amount of training data and exchange the LR coefficients in a privacy-preserving manner. The results show that the proposed policy can speed up the training time between 12% and 69%, compared to the traditional FL approach, with an average decrease in accuracy of 1.79% and 1.95%.

Item Type:	Book Section
Additional Information:	© 2025 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	07 Jan 2025 16:59
Last Modified:	07 Feb 2025 16:15
URI:	https://norma.ncirl.ie/id/eprint/7279

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