Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs

• URL: http://arxiv.org/abs/2505.13292v1
• Date: Mon, 19 May 2025 16:14:27 GMT
• Title: Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs
• Authors: Huaiying Luo, Cheng Ji,
• Abstract summary: We introduce a cross-cloud architecture in which federated learning works by aggregating model updates from decentralized nodes without exposing the original data.<n>We've further innovated by introducing a secure communication layer to ensure the privacy and integrity of model updates and training data.<n> Experimental results show that the proposed method is significantly better than the traditional federated learning model in terms of accuracy, convergence speed and data privacy protection.
• Score: 1.819979627431298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the age of cloud computing, data privacy protection has become a major challenge, especially when sharing sensitive data across cloud environments. However, how to optimize collaboration across cloud environments remains an unresolved problem. In this paper, we combine federated learning with large-scale language models to optimize the collaborative mechanism of AI systems. Based on the existing federated learning framework, we introduce a cross-cloud architecture in which federated learning works by aggregating model updates from decentralized nodes without exposing the original data. At the same time, combined with large-scale language models, its powerful context and semantic understanding capabilities are used to improve model training efficiency and decision-making ability. We've further innovated by introducing a secure communication layer to ensure the privacy and integrity of model updates and training data. The model enables continuous model adaptation and fine-tuning across different cloud environments while protecting sensitive data. Experimental results show that the proposed method is significantly better than the traditional federated learning model in terms of accuracy, convergence speed and data privacy protection.

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