小模型与大模型协同学习，兼顾速度、成本与隐私。
Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions
- 设备端小模型与云端大模型协作，分担计算任务
- 支持个性化服务，降低延迟与通信开销
- 适合移动端智能应用，如推荐系统和语音助手
传统云端大模型学习框架正面临延迟高、成本大、个性化差和隐私泄露等挑战。本文综述了一种新兴范式：设备端小模型与云端大模型协同学习，可在保障用户隐私的前提下实现低延迟、低成本、个性化的智能服务。我们从硬件、系统、算法和应用层进行全面回顾，总结学术界与工业界的最新进展。特别地，将协作算法分为基于数据、特征和参数三类。同时梳理了面向用户或设备级别的公开数据集与评估指标。还介绍了实际部署案例，涵盖推荐系统、移动直播和智能助手等场景。最后指出了该领域尚未解决的开放问题，以指导未来研究。
原文摘要 · Abstract (English)
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.
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