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.

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驱动力。这导致焦点转向边缘人工智能范式，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协调训练轮次和交换模型更新（即联邦学习），在边缘设备和服务器之间分割模型训练任务（即分割学习），要么通过八卦协议协调边缘设备之间的模型交换（即去中心化训练）。由于高度异构的学习器、配置、环境以及重大的同步挑战，这些方法不适合大规模分布式边缘学习。它们无法扩展大量学习者的规模，并且在长时间的训练中产生质量低下的模型。现代应用程序必须依赖提供及时、准确模型的系统。该项目通过提出对去中心化学习方法的彻底转变来解决这一差距。与 Uber 的交付服务类似，KUber 的目标是构建一种新颖的分布式架构，以促进学习实体之间所获得的知识的交换和交付。特别是，我们抓住机会将通用模型的训练任务与所学知识的共享任务解耦。这得益于边缘设备中嵌入的 AI/ML 加速器的进步以及高吞吐量和低延迟的 5G/6G 技术。 KUber 将彻底改变人工智能/机器学习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。