学院邀请英国纽卡斯尔大学Rajiv Ranjan教授做学术报告，欢迎广大师生踊跃参加！
题目：渗透式元学习方法：融合物联网、边缘计算与分布式学习
时间：2025年1月9日（周四） 16:00-18:00
地点：计算机与人工智能学院报告厅
报告人：Rajiv Ranjan（英国纽卡斯尔大学教授）
主讲人简介：
Rajiv Ranjan教授，英国纽卡斯尔大学讲席教授，欧洲科学院院士、IEEE和亚太人工智能协会会士，英国工程与自然科学研究委员会资助的国际中心（英国-澳大利亚）电动汽车安全与国家边缘人工智能中心创始主任。
Rajiv Ranjan教授是分布式系统领域国际公认的科学家，他因分布式系统（云计算、大数据和物联网）领域的研究而闻名。他担任包括IEEE Transactions on Computers（2014-2016）、IEEE Transactions on Cloud Computing、ACM Transactions on IoT、The Computer（牛津大学）、The Computing（Springer）和Future Generation Computer Systems等国际顶级期刊的编委。他负责顶级期刊IEEE Cloud Computing的“Blue Skies”板块（部门，2014-2019），主要职责是识别并撰写分布式系统研究领域内多个相互依存的研究学科交叉点上最重要的前沿研究问题，包括物联网、大数据分析、云计算和边缘计算。他是全球计算机科学和软件工程领域被高度引用的作者之一（h指数80+，g指数250+，谷歌学术引用32000+次，Scopus引用16000+次，Web of Science引用12000+次）。
讲座摘要：
物联网设备及其所产生的海量数据对我们的生活产生了深远影响，推动了多个应用领域（如医疗保健、智能电网、金融、灾害管理、农业、交通和水资源管理等）中关键下一代服务和应用的发展。分布式学习与训练等人工智能技术因大量多样化数据集的可用性而在多个物联网应用领域得到广泛应用。例如，在医疗诊断和预测方面，深度学习技术的进步显著提升了人类健康水平。然而，将大规模数据流及时可靠地传输（满足深度学习技术对高精度的要求）至集中式位置，如云数据中心，被认为是限制此类技术进一步扩展应用范围的关键瓶颈。
本次主题演讲将介绍研究整体分布式学习算法所面临的挑战和解决方案。主要内容包括：1.渗透计算范式的基础概念；2.云边渗透计算中编排复杂分布式学习算法的挑战；3.提出一种新方法，在全球成千上万台中型物联网和边缘设备上训练分布式深度学习模型；4.利用英国最大的物联网基础设施——城市观测站进行初步实验验证。
The Osmotic Meta-Learning Approach: Integrating Internet of Things, Edge Computing, and Distributed Learning
Abstract
Internet of Things devices, along with the large data volumes that such devices (can potentially) generate, can have a significant impact on our lives, fuelling the development of critical next-generation services and applications in a variety of application domains (e.g., health care, smart grids, finance, disaster management, agriculture, transportation, and water management). Artificial Intelligence technologies, such as Distributed Learning and Training, is finding application in multiple IoT application domains driven by the availability of diverse and large datasets. One such example is the advances in medical diagnostics and prediction that use deep learning technology to improve human health. However, timely and reliable transfer of large data streams (a requirement of deep learning technologies for achieving high accuracy) to centralized locations, such as cloud data centre environments, is being seen as a key limitation of expanding the application horizons of such technologies.
To this end, various paradigms, including osmotic computing, have been proposed that promote the distribution of data analysis tasks across cloud and edge computing environments. However, these existing paradigms fail to provide a detailed account of how technologies such as distributed deep learning can be orchestrated and take advantage of the cloud, edge, and mobile edge environments in a holistic manner. This keynote analyses different algorithmic and programming research challenges involved with the development of holistic and distributed learning algorithms that are resource and data-aware and can account for underlying heterogeneous data models, resource (cloud vs. edge vs. mobile edge) models, and data availability while executing—trading accuracy for execution time, etc.
Introduction to the fundamental concepts related to the Osmotic computing paradigm
Overview of the research and programming challenges involved with composing and orchestrating complex distributed learning algorithms and workflows in the (cloud-edge) Osmotic computing paradigm
Present a novel approach about how to train one Distributed Deep Learning (DDL) model on the hardware of thousands of mid-sized IoT and Edge devices across the world, rather than the use of GPU clusters available within a cloud data centre.
Discuss our initial experimental validation using the United Kingdom’s largest IoT infrastructure, namely, the Urban Observatory (http://www.urbanobservatory.ac.uk/)
Biography
Professor Rajiv Ranjan is an Australian-British computer scientist, of Indian origin, known for his research in Distributed Systems (Cloud Computing, Big Data, and the Internet of Things). He is University Chair Professor for the Internet of Things research in the School of Computing of Newcastle University, United Kingdom. He is an internationally established scientist in the area of Distributed Systems (having published about 350 scientific papers). He is a fellow of IEEE (2024), Academia Europaea (2022) and the Asia-Pacific Artificial Intelligence Association (2023). He is also the Founding Director of the International Centre (UK-Australia) on the EV Security and National Edge Artificial Intelligence Hub, both funded by EPSRC. He has secured more than $68 Million AUD (£34 Million+ GBP) in the form of competitive research grants from both public and private agencies. He is an innovator with strong and sustained academic and industrial impact and a globally recognized R&D leader with a proven track record. He serves on the editorial boards of top quality international journals including IEEE Transactions on Computers (2014-2016), IEEE Transactions on Cloud Computing, ACM Transactions on the Internet of Things, The Computer (Oxford University), and The Computing (Springer) and Future Generation Computer Systems. He led the Blue Skies section (department, 2014-2019) of IEEE Cloud Computing, where his principal role was to identify and write about the most important, cutting-edge research issues at the intersection of multiple, inter-dependent research disciplines within distributed systems research area including Internet of Things, Big Data Analytics, Cloud Computing, and Edge Computing. He is one of the highly cited authors in computer science and software engineering worldwide (h-index=80+, g-index=250+, and 32000+ google scholar citations, h-index=60+ and 16000+ Scopus citations, and h-index=50+ and 12000+ Web of Science citations).
