Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning

2025-03-15

AI Summary

Privacy leakage and data security risks pose critical challenges in cross-cloud large language model (LLM) deployment and training. Method: This paper proposes a privacy-preserving federated learning framework for multi-cloud environments, featuring a dynamic model aggregation mechanism and a hybrid aggregation scheme that integrates advanced cryptographic primitives with standardized cross-cloud data collaboration protocols—all while ensuring raw data remains localized at edge devices. Contribution/Results: The framework achieves a 23% improvement in training efficiency and a 1.8% gain in model accuracy over conventional federated learning, alongside significantly enhanced convergence stability. It enables secure, efficient, and scalable cross-cloud collaborative training without compromising data sovereignty or model utility.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsComputer Vision: Large Vision Models

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSecurity and Privacy: Large-scale security measurementsSearch and Retrieval-Augmented AI: Large language models for search

Abstract

The fast development of large language models (LLMs) and popularization of cloud computing have led to increasing concerns on privacy safeguarding and data security of cross-cloud model deployment and training as the key challenges. We present a new framework for addressing these issues along with enabling privacy preserving collaboration on training between distributed clouds based on federated learning. Our mechanism encompasses cutting-edge cryptographic primitives, dynamic model aggregation techniques, and cross-cloud data harmonization solutions to enhance security, efficiency, and scalability to the traditional federated learning paradigm. Furthermore, we proposed a hybrid aggregation scheme to mitigate the threat of Data Leakage and to optimize the aggregation of model updates, thus achieving substantial enhancement on the model effectiveness and stability. Experimental results demonstrate that the training efficiency, privacy protection, and model accuracy of the proposed model compare favorably to those of the traditional federated learning method.

Problem

• Addressing privacy and security in cross-cloud LLM training
• Enhancing federated learning with cryptographic and aggregation techniques
• Mitigating data leakage and optimizing model update aggregation

Innovation

• Federated learning for cross-cloud privacy protection
• Dynamic model aggregation with cryptographic primitives
• Hybrid aggregation to prevent data leakage