Cloud Collaborative Learning

Cloud collaborative learning focuses on improving the performance and personalization of machine learning models by leveraging the computational power of the cloud while preserving data privacy and addressing resource limitations on edge devices. Current research emphasizes efficient model splitting and knowledge distillation techniques, often employing multimodal large language models or large vision models, to enable collaborative training between cloud and device without directly transferring raw data. This approach is significant for deploying large models on resource-constrained devices, enhancing model personalization in applications like recommender systems, and improving overall model accuracy and efficiency.