With the remarkable progress of natural language processing technology, pre-trained language models have emerged as powerful tools, showcasing outstanding performance across a diverse range of application scenarios. Nevertheless, the training of these sophisticated models often presents substantial challenges, demanding an enormous amount of computational resources and advanced data processing capabilities. Multi-cloud collaborative training, a novel paradigm, provides an innovative solution to surmount the limitations of relying solely on a single cloud platform. It enables the integration of computational resources from multiple clouds, allowing them to work in unison to accomplish the intricate training tasks of large-scale language models. This research delves into the core technologies underlying multi-cloud collaborative training. It meticulously examines aspects such as data segmentation and allocation strategies, which are crucial for effectively distributing the training data among different clouds while maintaining data integrity and representativeness. Communication optimization techniques are also explored in detail, aiming to minimize the latency and bandwidth consumption during the exchange of model parameters and gradients between clouds. Additionally, the study focuses on the development and evaluation of model aggregation algorithms that can seamlessly combine the local models trained on each cloud to form a globally optimal model. Moreover, the compatibility issues among heterogeneous cloud platforms, including differences in infrastructure, software environments, and service interfaces, are thoroughly analyzed, and corresponding solutions are proposed. Furthermore, this study places great emphasis on data security and privacy protection in the context of multi-cloud training. It investigates various strategies to safeguard the confidentiality and integrity of data during the training process. Specifically, the application of advanced data encryption techniques, such as homomorphic encryption and secure multi-party computation, is explored to ensure that data can be shared and processed across clouds without compromising its privacy. Differential privacy methods are also examined to add noise to the data in a controlled manner, protecting individual data records while still enabling accurate model training.