With the rapid development of natural language processing technology, large language models have demonstrated exceptional performance in various application scenarios. However, training these models requires significant computational resources and data processing capabilities. Cross-cloud federated training offers a new approach to addressing the resource bottlenecks of a single cloud platform, allowing the computational resources of multiple clouds to collaboratively complete the training tasks of large models. This study analyzes the key technologies of cross-cloud federated training, including data partitioning and distribution, communication optimization, model aggregation algorithms, and the compatibility of heterogeneous cloud platforms. Additionally, the study examines data security and privacy protection strategies in cross-cloud training, particularly the application of data encryption and differential privacy techniques. Through experimental validation, the proposed technical framework demonstrates enhanced training efficiency, ensured data security, and reduced training costs, highlighting the broad application prospects of cross-cloud federated training.

翻译：随着自然语言处理技术的快速发展，大语言模型在各类应用场景中展现出卓越性能。然而，训练此类模型需要巨大的计算资源和数据处理能力。跨云联邦训练为解决单一云平台的资源瓶颈提供了新思路，能够聚合多个云平台的计算资源协同完成大模型的训练任务。本研究分析了跨云联邦训练的关键技术，包括数据划分与分发、通信优化、模型聚合算法以及异构云平台的兼容性。此外，研究探讨了跨云训练中的数据安全与隐私保护策略，特别是数据加密与差分隐私技术的应用。通过实验验证，所提出的技术框架在提升训练效率、保障数据安全以及降低训练成本方面表现出色，展现了跨云联邦训练的广阔应用前景。