KUber: Knowledge Delivery System For Machine Learning At Scale

AI/ML系统正在成为用户产品和应用程序的一个组成部分，也是大多数组织的主要收入来源。这导致将重点转移到Edge AI范式，因为Edge设备拥有训练模型所需的数据。Main Edge AI方法要么通过中央服务器(即，联合学习)协调训练轮次并交换模型更新，要么在边缘设备和服务器之间分担模型训练任务(即，拆分学习)，或者通过八卦协议(即，分散训练)协调边缘设备之间的模型交换。由于学习者、配置、环境的高度异构性以及巨大的同步挑战，这些方法不适合大规模的分布式边缘学习。它们无法适应大量学习者的规模，并且在长时间的培训时间内产生质量较低的模型。对于现代应用程序来说，依赖于提供及时和准确的模型的系统是必不可少的。该项目通过提出一种向分散学习方法的彻底转变来解决这一差距。与优步的交付服务类似，Kuber的目标是构建一个新的分布式架构，以促进学习实体之间获得的知识的交换和交付。特别是，我们抓住机会将共同模型的训练任务与学习知识的共享任务脱钩。这是由于嵌入在边缘设备中的AI/ML加速器以及高吞吐量和低延迟的5G/6G技术的进步。Kuber将彻底改变AI/ML方法在日常生活应用中的使用，并为用户、组织和政府之间灵活、可扩展和高效的协作学习打开大门。

AI/ML systems are becoming an integral part of user products and applications as well as the main revenue driver for most organizations. This resulted in shifting the focus toward the Edge AI paradigm as edge devices possess the data necessary for training the models. Main Edge AI approaches either coordinate the training rounds and exchange model updates via a central server (i.e., Federated Learning), split the model training task between edge devices and a server (i.e., split Learning), or coordinate the model exchange among the edge devices via gossip protocols (i.e., decentralized training). Due to the highly heterogeneous learners, configurations, environment as well as significant synchronization challenges, these approaches are ill-suited for distributed edge learning at scale. They fail to scale with a large number of learners and produce models with low qualities at prolonged training times. It is imperative for modern applications to rely on a system providing timely and accurate models. This project addresses this gap by proposing a ground-up transformation to decentralized learning methods. Similar to Uber's delivery services, the goal of KUber is to build a novel distributed architecture to facilitate the exchange and delivery of acquired knowledge among the learning entities. In particular, we seize an opportunity to decouple the training task of a common model from the sharing task of learned knowledge. This is made possible by the advances in the AI/ML accelerators embedded in edge devices and the high-throughput and low-latency 5G/6G technologies. KUber will revolutionize the use of AI/ML methods in daily-life applications and open the door for flexible, scalable, and efficient collaborative learning between users, organizations, and governments.