The scale of the global edge AI market continues to grow. The current
technical challenges that hinder the large-scale replication of edge AI are
mainly small samples on the edge and heterogeneity of edge data. In addition,
edge AI customers often have requirements for data security compliance and
offline autonomy of edge AI services. Based on the lifelong learning method in
the academic world, we formally define the problem of edge-cloud collaborative
lifelong learning for the first time, and release the industry's first
open-source edge-cloud collaborative lifelong learning. Edge-cloud
collaborative lifelong learning adapts to data heterogeneity at different edge
locations through (1) multi-task transfer learning to achieve accurate
prediction of "thousands of people and thousands of faces"; (2) incremental
processing of unknown tasks, the more systems learn and the smarter systems are
with small samples, gradually realize AI engineering and automation; (3) Use
the cloud-side knowledge base to remember new situational knowledge to avoid
catastrophic forgetting; (4) The edge-cloud collaborative architecture enables
data security compliance and edge AI services to be offline autonomy while
applying cloud resources. This work hopes to help fundamentally solve the
above-mentioned challenges of edge-cloud collaborative machine learning.

基于学术界的终身学习方法，我们首次正式定义了边缘 - 云协作终身学习问题，并发布了业界首个开源边缘 - 云协作终身学习。该方法通过多任务迁移学习、增量处理、云侧知识库记忆和边缘 - 云协作架构实现了数据异构性适应、具有小样本更加智能的系统以及边缘数据安全合规和离线自主等特性，从根本上解决了边缘 - 云协作机器学习面临的挑战。