International Journal Papier Volume 4, Issue 3(Page 001-007) Advance and Scientific Review ISSN: 2709-0248 1 Copyright © 2023, International Journal Papier Advance and Scientific Review, Under the license CC BY-SA 4.0 DOI: https://doi.org/10.47667/ijpasr.v4i3.235 Received : September 7, 2023 Received in Revised: October 25, 2023 Accepted: October 28, 2023 Secure Federated Learning with a Homomorphic Encryption Model Yasmin Makki Mohialden 1 , Nadia Mahmood Hussien 1 , Saba Abdulbaqi Salman 2 , Mohammad Aljanabi 2 1 Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq 2 Department of Computer Science, College of Education, Al-Iraqia University, Baghdad, Iraq Abstract Federated learning (FL) offers collaborative machine learning across decentralized devices while safeguarding data privacy. However, data security and privacy remain key concerns. This paper introduces "Secure Federated Learning with a Homomorphic Encryption Model," addressing these challenges by integrating homomorphic encryption into FL. The model starts by initializing a global machine learning model and generating a homomorphic encryption key pair, with the public key shared among FL participants. Using this public key, participants then collect, preprocess, and encrypt their local data. During FL Training Rounds, participants decrypt the global model, compute local updates on encrypted data, encrypt these updates, and securely send them to the aggregator. The aggregator homomorphic ally combines updates without revealing participant data, forwarding the encrypted aggregated update to the global model owner. The Global Model Update ensures the owner decrypts the aggregated update using the private key, updates the global model, encrypts it with the public key, and shares the encrypted global model with FL participants. With optional model evaluation, training can iterate for several rounds or until convergence. This model offers a robust solution to Florida data privacy and security issues, with versatile applications across domains. This paper presents core model components, advantages, and potential domain-specific implementations while making significant strides in addressing FL's data privacy concerns. Keywords: Secure Federated Learning; Homomorphic Encryption; Data Privacy; Model Security; Collaborative Machine Learning Introduction Federated Learning (FL) has transformed collaborative machine learning by enabling decentralized model training while safeguarding data privacy (Baracaldo & Shaul, 2023; NVIDIA, 2022; Jin, 2023; Wibawa et al., 2023; IEEE, 2021). This paradigm holds promise across diverse domains such as healthcare, finance, and the Internet of Things (IoT). Nonetheless, Florida encounters formidable privacy and security challenges hindering its widespread adoption. This paper introduces a pioneering approach, "Secure Federated Learning with a Homomorphic Encryption Model," which confronts these challenges by integrating homomorphic encryption techniques into the FL framework. Our approach originally merges two robust technologies, federated learning, and homomorphic encryption, yielding notable contributions: