跨云联邦训练让大模型在多云环境下高效安全地协同训练。
Research on Key Technologies for Cross-Cloud Federated Training of Large Language Models
- 通过多云资源协同，解决单云算力不足问题。
- 实验验证框架提升训练效率并降低训练成本。
- 适合关注大模型训练安全与分布式部署的开发者。
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中展现出卓越性能，但其训练需大量计算资源与数据处理能力。跨云联邦训练为突破单一云平台资源瓶颈提供了新路径，可协同多个云平台的计算资源完成大模型训练任务。本研究分析了跨云联邦训练的关键技术，包括数据分区与分发、通信优化、模型聚合算法以及异构云平台兼容性。同时，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。实验结果表明，所提出的框架在提升训练效率、保障数据安全及降低训练成本方面表现优异，凸显了跨云联邦训练广阔的推广应用前景。
原文摘要 · Abstract (English)
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.
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