Advancing Privacy and Accuracy with Federated Learning and Homomorphic Encryption
In this paper, we present an integrated framework that combines Federated Learning (FL) with Homomorphic Encryption (HE) using the Artificial Intelligence (AI) models and the Cheon-Kim-Kim-Song (CKKS) algorithm to address the challenges of privacy and accuracy. FL facilitates collaborative training of Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models across decentralized devices, allowing for data privacy preservation without sharing raw data. The integration of the CKKS algorithm for HE ensures secure computation on encrypted data during the FL process. Our experimental results on three diverse datasets demonstrate the efficacy of this approach, achieving an impressive highest average accuracy of 97.3%. Additionally, the CKKS algorithm is used to achieve efficient computation, making it a promising solution for privacy-conscious machine learning applications, and paving the way for practical deployment in various real-world scenarios, thereby revolutionizing the landscape of privacy-preserving machine learning.
Email Address of Submitting Authornguyentantuy@gmail.com
ORCID of Submitting Author0000-0002-9485-7720
Submitting Author's InstitutionNorthern Arizona Unviersity
Submitting Author's Country
- United States of America