Decentralized Training for Secure Network Security: Leveraging RFB Protocol and Federated Learning in Zero Trust Network

This study proposes the implementation of a Remote Frame Buffer (RFB) gateway in a zero-trust network using Federated Learning. The proposed approach allows multiple remote devices to collaboratively train a machine learning model without the need for centralized data storage, and without compromising the privacy of the data owners. The study includes an evaluation of the effectiveness of RFB with and without Federated Learning, as well as the effectiveness of local and global datasets and Gradient Stochastic Descent. The results demonstrate that the use of Federated Learning with RFB protocol is more effective than using RFB without Federated Learning for training machine learning models. The decentralized training process ensures that the data of individual devices remains protected, without the need for centralized data storage. Homomorphic Encryption and Differential Privacy techniques can be incorporated into the decentralized training process to enhance security.