Improving Data Security and Verification in Federated Learning with Homomorphic Encryption: Literature Review: Subtitle - Alfina Tiur Mida Sitanggang; Eddy Refianto Eddy; Ferry Muhamad Ramadhan; Frangky (Global Science: Journal of Information Technology and Computer Science, 2026)
Federated Learning has emerged as a prominent solution for collaboratively training machine learning models without sharing raw data, thereby preserving user privacy in today's digital era. However, threats such as man-in-the-middle attacks, data reconstruction attacks, and model manipulation remain significant challenges for this approach. This literature review explores the integration of Homomorphic Encryption (HE) into Federated Learning to enhance data security and ensure model integrity verification. The findings indicate that the combination of Homomorphic Encryption and Federated Learning can reduce the risk of data leakage by up to 90% compared to conventional non-encrypted methods. Furthermore, despite introducing a computational overhead of approximately 20–30%, model accuracy remains relatively high, with only a 1–2% reduction. This study contributes to the development of a more secure, efficient, and reliable Federated Learning framework for critical applications, including healthcare, finance, and the Internet of Things (IoT). Keywords: Federated Learning, Homomorphic Encryption, Data Security, Model Verification, Privacy Protection.