Research on Key Technologies for Cross-Cloud Federated Training of Large Language Models

These models have achieved remarkable success in areas such as machine translation, speech recognition, and text generation. However, training these large models typically requires vast computational resources and data, which not only places high demands on the resources of a single cloud platform but can also lead to computational bottlenecks, latency issues, and cost pressures[1]. Cross-cloud federated training has emerged as an effective solution to these challenges. By leveraging the computational resources of multiple cloud platforms, cross-cloud federated training enables distributed processing of large datasets and synchronous model parameter updates, thereby accelerating the training process. The implementation of cross-cloud federated training involves addressing several key technical challenges, including efficiently allocating and managing the computational resources of cloud platforms, optimizing data communication between clouds, and ensuring data privacy and security during the training process[2].