Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917994198728704 |
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| author | Niu, Chaoyue Ding, Yucheng Lu, Junhui Huang, Zhengxiang Zeng, Hang Dai, Yutong Tu, Xuezhen Lv, Chengfei Wu, Fan Chen, Guihai |
| author_facet | Niu, Chaoyue Ding, Yucheng Lu, Junhui Huang, Zhengxiang Zeng, Hang Dai, Yutong Tu, Xuezhen Lv, Chengfei Wu, Fan Chen, Guihai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15300 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions Niu, Chaoyue Ding, Yucheng Lu, Junhui Huang, Zhengxiang Zeng, Hang Dai, Yutong Tu, Xuezhen Lv, Chengfei Wu, Fan Chen, Guihai Machine Learning Distributed, Parallel, and Cluster Computing Multiagent Systems 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. |
| title | Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing Multiagent Systems |
| url | https://arxiv.org/abs/2504.15300 |