Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions
Journal:
arXiv
Published Date:
Apr 17, 2025
Abstract
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.
