ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous
  Environment Adaptation
Yan Zhuang Zhenzhe Zheng Yunfeng Shao Bingshuai Li Fan Wu Guihai Chen
Pervasive mobile AI applications primarily employ one of the two learning
paradigms: cloud-based learning (with powerful large models) or on-device
learning (with lightweight small models). Despite their own advantages, neither
paradigm can effectively handle dynamic edge environments with frequent data
distribution shifts and on-device resource fluctuations, inevitably suffering
from performance degradation. In this paper, we propose ECLM, an edge-cloud
collaborative learning framework for rapid model adaptation for dynamic edge
environments. We first propose a novel block-level model decomposition design
to decompose the original large cloud model into multiple combinable modules.
By flexibly combining a subset of the modules, this design enables the
derivation of compact, task-specific sub-models for heterogeneous edge devices
from the large cloud model, and the seamless integration of new knowledge
learned on these devices into the cloud model periodically. As such, ECLM
ensures that the cloud model always provides up-to-date sub-models for edge
devices. We further propose an end-to-end learning framework that incorporates
the modular model design into an efficient model adaptation pipeline including
an offline on-cloud model prototyping and training stage, and an online
edge-cloud collaborative adaptation stage. Extensive experiments over various
datasets demonstrate that ECLM significantly improves model performance (e.g.,
18.89% accuracy increase) and resource efficiency (e.g., 7.12x communication
cost reduction) in adapting models to dynamic edge environments by efficiently
collaborating the edge and the cloud models.