KUber: Knowledge Delivery System For Machine Learning At Scale

KUber：大规模机器学习的知识传递系统

• 批准号: EP/X035085/1
• 负责人: Ahmed Mohamed Abdelmoniem Sayed
• 金额: $ 66.61万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX035085%2F1
• 关键词: KUber; Knowledge; Delivery; System; Machine

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.

AI/ML系统正在成为用户产品和应用程序的组成部分，以及大多数组织的主要收入驱动力。这导致将焦点转移到边缘AI范式上，因为边缘设备具有训练模型所需的数据。 Main Edge AI接近通过中央服务器（即联合学习）来协调训练回合和交换模型更新，在边缘设备和服务器之间将模型培训任务分开（即拆分学习），或者通过八卦设备之间的模型交换（即，即处于分发的培训）。由于高度异质的学习者，配置，环境以及重大同步挑战，这些方法不适合按大规模分布式边缘学习。他们无法与大量的学习者进行扩展，并在长时间的培训时间生产具有低品质的模型。现代应用程序必须依靠提供及时，准确的模型的系统。该项目通过提出对分散学习方法的基础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体系结构，以促进学习实体之间获得的知识的交流和交付。特别是，我们抓住机会将通用模型的培训任务与学习知识的共享任务中。通过嵌入边缘设备以及高通量和低延迟5G/6G技术的AI/ML加速器的进步，这是可能的。 Kuber将彻底改变在日常生活应用中使用AI/ML方法，并为用户，组织和政府之间的灵活，可扩展和有效的协作学习打开大门。