KUber: Knowledge Delivery System For Machine Learning At Scale

人工智能/机器学习系统正在成为用户产品和应用程序不可或缺的一部分，也是大多数组织的主要收入来源。这导致将焦点转移到边缘人工智能范式，因为边缘设备拥有训练模型所需的数据。主要边缘人工智能方法要么通过中央服务器（即联邦学习）协调训练回合和交换模型更新，要么在边缘设备和服务器之间分割模型训练任务（即分裂学习），要么通过八卦协议（即分散训练）协调边缘设备之间的模型交换。由于高度异构的学习者、配置、环境以及显著的同步挑战，这些方法不适合大规模的分布式边缘学习。它们不能在大量学习者的情况下进行扩展，并且在长时间的训练中产生低质量的模型。现代应用必须依赖于一个提供及时和准确模型的系统。这个项目通过提出一种从根本上向分散学习方法的转变来解决这一差距。与Uber的交付服务类似，KUber的目标是建立一种新颖的分布式架构，以促进学习实体之间获得的知识的交换和交付。特别是，我们抓住机会将公共模型的训练任务与学习知识的共享任务解耦。这是通过嵌入边缘设备的AI/ML加速器的进步以及高吞吐量和低延迟的5G/6G技术实现的。KUber将彻底改变人工智能/机器学习方法在日常应用中的使用，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.