Secure Training Protocols for Machine Learning Models in Adversarial Network

In the realm of secure training protocols for machine learning models within adversarial networks, the proposed methodology encompasses three key algorithms: Federated Learning with Differential Privacy (FedDP), Adversarial Training with Robust Optimization (AdvRO), and Privacy-Preserving Homomorphic Encryption Training (PPHET). Each algorithm plays a critical role in enhancing data privacy, model robustness, and reliability during the model training process. The study evaluates these methods in comparison to traditional approaches, emphasizing the importance of data privacy, robustness, and computational efficiency. This research presents a secure training protocol for machine learning models in adversarial networks, with a focus on data privacy, model robustness, and reliability assurance. The proposed method combines federated learning with differential privacy, adversarial training with robust optimization, and privacy-preserving homomorphic encryption training. These approaches ensure data privacy, enhance model robustness, and protect against adversarial threats. We compare the proposed method with traditional methods and evaluate their performance across various parameters, such as data privacy, robustness to adversarial attacks, and scalability. The results demonstrate the effectiveness of the proposed approach in achieving secure and trustworthy machine learning models in adversarial networks.