Federated Learning-Based Data Collaboration Method for Enhancing Edge Cloud AI System Security Using Large Language Models

TL;DR

This paper introduces a federated learning approach enhanced with large language models and secure multi-party computation to improve data privacy and robustness in edge cloud AI systems, demonstrating significant security improvements.

Contribution

It presents a novel federated learning framework integrating LLMs and secure protocols to enhance privacy and security in edge cloud AI systems.

Findings

15% improvement in data protection over traditional methods

Enhanced resistance to data leakage and model poisoning

Improved system robustness and efficiency

Abstract

With the widespread application of edge computing and cloud systems in AI-driven applications, how to maintain efficient performance while ensuring data privacy has become an urgent security issue. This paper proposes a federated learning-based data collaboration method to improve the security of edge cloud AI systems, and use large-scale language models (LLMs) to enhance data privacy protection and system robustness. Based on the existing federated learning framework, this method introduces a secure multi-party computation protocol, which optimizes the data aggregation and encryption process between distributed nodes by using LLM to ensure data privacy and improve system efficiency. By combining advanced adversarial training techniques, the model enhances the resistance of edge cloud AI systems to security threats such as data leakage and model poisoning. Experimental results show that the proposed method is 15% better than the traditional federated learning method in terms of data protection and model robustness.