The conventional cloud-based large model learning framework is increasingly constrained by latency, cost, personalization, and privacy concerns. In this survey, we explore an emerging paradigm: collaborative learning between on-device small model and cloud-based large model, which promises low-latency, cost-efficient, and personalized intelligent services while preserving user privacy. We provide a comprehensive review across hardware, system, algorithm, and application layers. At each layer, we summarize key problems and recent advances from both academia and industry. In particular, we categorize collaboration algorithms into data-based, feature-based, and parameter-based frameworks. We also review publicly available datasets and evaluation metrics with user-level or device-level consideration tailored to collaborative learning settings. We further highlight real-world deployments, ranging from recommender systems and mobile livestreaming to personal intelligent assistants. We finally point out open research directions to guide future development in this rapidly evolving field.
View on arXiv@article{niu2025_2504.15300,
title={ Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions },
author={ Chaoyue Niu and Yucheng Ding and Junhui Lu and Zhengxiang Huang and Hang Zeng and Yutong Dai and Xuezhen Tu and Chengfei Lv and Fan Wu and Guihai Chen },
journal={arXiv preprint arXiv:2504.15300},
year={ 2025 }
}