Research on privacy protection and information security of large models based on federated learning algorithm

With the increasing reliance on large machine learning models across various industries, concerns regarding privacy and information security have become critical. Federated Learning (FL) has emerged as a promising solution for training machine learning models while preserving data privacy by keeping data decentralized. This paper explores the use of Federated Learning algorithms for privacy protection and information security in the context of large-scale models, including scenarios involving optical sensing data from cameras, LiDAR, and other optical measurement devices. We propose a framework that leverages FL to ensure that sensitive data, including high-resolution optical imagery, remains local, reducing the risk of data leakage during model training. The study examines key challenges in implementing FL, including model aggregation, secure communication, and defense against potential attacks such as model inversion and data poisoning. We also present a novel security-enhanced federated learning protocol designed to improve the robustness of large models against adversarial threats, particularly when processing multi-modal optical data. Experimental results demonstrate that our approach effectively balances privacy protection with model performance, achieving competitive accuracy while safeguarding sensitive information. The findings of this research provide valuable insights into the secure deployment of large models in real-world applications, including those relying on optical sensing, contributing to the advancement of privacy-preserving AI technologies.