Research on Key Technologies for Cross-Cloud Federated Training of Large Language Models
It addresses computational and data processing limitations for AI researchers and practitioners, but is incremental as it builds on existing federated learning concepts.
This study tackles the resource bottlenecks in training large language models by proposing a cross-cloud federated training framework, which enhances training efficiency, ensures data security, and reduces costs as validated experimentally.
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.