Title: Dual-Mode Cloud-Device Collaboration for Efficient Continual Adaptation
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Continual Domain Adaptation, Test-Time Adaptation, Cloud-device Collaboration

Abstract: Continuous environmental changes induce distribution shifts, leading to significant performance 
degradation of models deployed on resource-constrained mobile devices. Existing fast adaptation 
methods fail to provide sufficient generalization to meet performance requirements, 
while cloud-device collaborative learning often relies on a considerable amount of data, 
limiting real-time applicability. To ensure both timeliness and effectiveness, we propose 
a dual-mode cloud-device collaborative framework. Specifically, the proposed mothod dynamically 
switches modes according to the degree of distribution shift: (1) Collaborative adaptation 
mode handles substantial shifts, where the cloud performs multi-level domain alignment and 
position-aware prompting to learn domain-invariant representations, which are then distilled 
to the device model; (2) Self-adaptation mode addresses minor shifts, where the device model 
performs unsupervised test-time adaptation with pseudo-label generation and quality-aware 
reweighting for fast local updates. Experimental results show that our framework achieves superior 
performance while using only 80\% of the data and incurring less than 0.5\% additional parameters 
and computation. Moreover, it consistently outperforms compared methods in both accuracy and 
single-frame inference speed.