KubeEdge-Sedna v0.3：迈向下一代自动定制化AI工程方案
KubeEdge-Sedna v0.3: Towards Next-Generation Automatically Customized AI Engineering Scheme
AI总结：
本文首次定义边云协同终身学习问题并发布开源方案KubeEdge-Sedna v0.3，通过多任务迁移、增量学习、知识库记忆和边云协同架构解决边缘小样本、数据异构、安全合规与离线自主等挑战。
摘要：
全球边缘AI市场规模持续增长。当前阻碍边缘AI大规模复制的技术挑战主要是边缘小样本和边缘数据异构性。此外，边缘AI客户通常对数据安全合规和边缘AI服务的离线自主性有要求。基于学术界的终身学习方法，我们首次正式定义了边云协同终身学习问题，并发布了业界首个开源边云协同终身学习方案。边云协同终身学习通过以下方式适应不同边缘位置的数据异构性：（1）多任务迁移学习，实现“千人千面”的精准预测；（2）对未知任务进行增量处理，系统学习越多，在小样本下越智能，逐步实现AI工程化与自动化；（3）利用云侧知识库记住新的情境知识，避免灾难性遗忘；（4）边云协同架构在应用云资源的同时，使数据安全合规和边缘AI服务能够离线自主运行。本工作希望从根本上帮助解决上述边云协同机器学习的挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
