Rajiv Ranjan, Newcastle University

The Osmotic Meta-Computing Approach: Integrating Internet of Things, Edge Computing, and Distributed Learning


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 250 scientific papers). He has secured more than $32 Million AUD (£16 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=67, g-index=216, and 23000+ google scholar citations, h-index=35 and 7300+ Scopus citations).


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.

  1. Introduction to the fundamental concepts related to the Osmotic computing paradigm
  2. 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
  3. 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.
  4. Discuss our initial experimental validation using the United Kingdom’s largest IoT infrastructure, namely, the Urban Observatory (

Fenghua Li, Institute of Information Engineering, CAS






Jie Wu, Temple University

Edge-Cloud Networks for Efficient AI/ML Implementations


Jie Wu is Laura H. Carnell Professor at Temple University and the Director of the Center for Networked Computing (CNC). He served as Chair of the Department of Computer and Information Sciences from the summer of 2009 to the summer of 2016 and Associate Vice Provost for International Affairs from the fall of 2015 to the summer of 2017. Prior to joining Temple University, he was a program director at the National Science Foundation and was a distinguished professor at Florida Atlantic University, where he received his Ph.D. in 1989. His current research interests include mobile computing and wireless networks, routing protocols, network trust and security, distributed algorithms, applied machine learning, and cloud computing. Dr. Wu regularly published in scholarly journals, conference proceedings, and books. He serves on several editorial boards, including IEEE Transactions on Service Computing and Journal of Computer Science and Technology. Dr. Wu is/was general chair/co-chair for IEEE DCOSS’09, IEEE ICDCS’13, ICPP’16, IEEE CNS’16, WiOpt’21, ICDCN’22, IEEE IPDPS'23, and ACM MobiHoc'23 as well as program chair/cochair for IEEE MASS’04, IEEE INFORCOM’11, CCF CNCC’13, and ICCCN’20. He was an IEEE Computer Society Distinguished Visitor, ACM Distinguished Speaker, and chair for the IEEE Technical Committee on Distributed Processing (TCDP). Dr. Wu is a Fellow of the AAAS and a Fellow of the IEEE. He is the recipient of the 2011 China Computer Federation (CCF) Overseas Outstanding Achievement Award. He is a Member of the Academia Europaea (MAE). Dr. Wu is currently on leave working at China Telecom as a scientist in cloud computing.


Edge-cloud networks connect a wide range of systems and devices, ranging from large data centers to small IoT devices. The potential advantages of adopting edge and cloud networking include the quick adaptation of new technologies, including a faster rollout and adoption of software and feature updates, as well as better management of various resources, including network and edge devices. This talk provides an overview of some challenges in efficient support for running AI/ML algorithms in edge-cloud networks. Our focus is on low latency, connectivity, and local data processing while still achieving efficiency. We will look at two specific examples of AI/ML implementations: one is the optimal offloading of AI/ML code from IoT/edge devices to the cloud, and the other is exploring network topology and connectivity for efficient decentralized federated learning.

Gang Qu, University of Maryland

Hardware-Assisted Authentication in Meta Computing


Dr. Gang Qu is a professor in the Department of Electrical and Computer Engineering at the University of Maryland, College Park, where he leads the Maryland Embedded Systems and Hardware Security Lab (MeshSec) and the Wireless Sensor Laboratory. His recent research activities are on hardware security and trust, artificial intelligence, security in vehicular systems, and the Internet of Things. He is also known for his work on wireless sensor networks, low power and energy efficient embedded system design.

Dr. Qu has served as the general chair or program chair for 20 conferences/symposiums/workshops. He is/was an active associated editor for IEEE TC, IEEE TCAD, IEEE TCAS II, IEEE TETC, IEEE TVLSI, ACM TODAES, Integration the VLSI Journal, and JCST. Dr. Qu is an enthusiastic teacher and has taught many security courses, including a popular MOOC on Hardware Security through Coursera. He co-founded the AsianHOST symposium (2016) and the IEEE CEDA hardware security and trust technique committee (HSTTC, 2020). He worked as an expert and a program director in the NSF Secure and Trustworthy Cyberspace (SaTC) program (2021-2023). He is a fellow of IEEE.


In meta computing, multiple computing resources are integrated to facilitate a variety of applications. Security and trust are crucial for meta computing, but many resources like CPU, memory, and battery power might be limited that they cannot afford the classic cryptographic security solutions. In this talk, we use authentication as an example to demonstrate how hardware and physical characteristics can help to build lightweight security primitives such as authentication protocols for meta computing. More specifically, we will report our recent work that utilizes the traditional CMOS, the emerging RRAM technologies, and voltage over scaling (VoS) technique for the authentication of device, user and computation. These practical approaches are promising alternatives for the classical crypto-based authentication protocols for the embedded and IoT devices in meta computing environment.

My Thai, University of Florida

Federated Learning: Big Promise or False Hope?


My T. Thai is a University of Florida (UF) Research Foundation Professor, Associate Director of UF Nelms Institute for the Connected World, and a Fellow of IEEE and AAIA. Dr. Thai is a leading authority who has done transformative research in Trustworthy AI and Optimization, especially for complex systems with applications to healthcare, social media, critical networking infrastructure, and cybersecurity. The results of her work have led to 7 books and 350+ publications in highly ranked international journals and conferences, including several best paper awards from the IEEE, ACM, and AAAI.

In responding to a world-wide call of responsible and safety AI, Dr. Thai is a pioneer in designing deep explanations for black-box ML models, while defending against explanation-guided attacks, evident by her Distinguished Papers Award at the Association for the Advancement of Artificial Intelligence (AAAI) conference on AI, 2023. At the same year, she was also awarded an ACM Web Science Trust Test-of-Time award, for her landmark work on combating misinformation in social media. In 2022, she received an IEEE Big Data Security Women of Achievement Award. In 2009, she was awarded the Young Investigator (YIP) from the Defense Threat Reduction Agency (DTRA) and in 2010, she won the NSF CAREER Award. She is presently the Editor-in-Chief of Springer Journal of Combinatorial Optimization, IET Blockchain Journal, and book series editor of Springer Optimization and Its Applications.


Federated Learning (FL) has emerged as a promising large-scale collaborative learning framework for its potential to protect user privacy and security. However, this promise has been constantly challenged. In this talk, we show that FL in its primitive form offers little to no privacy and security protection, by analyzing several attack vectors, both from malicious users to a dishonest server. Even with a layer of protection from differential privacy and secure aggregation, we further demonstrate that current FL implementation provides no guarantee on privacy and security, thus calling for a fundamental re-design.

Falko Dressler, TU Berlin

Virtualized Edge Computing as a Basis for Edge AI


Falko Dressler is full professor and Chair for Telecommunication Networks at the School of Electrical Engineering and Computer Science, TU Berlin. He received his M.Sc. and Ph.D. degrees from the Dept. of Computer Science, University of Erlangen in 1998 and 2003, respectively. Dr. Dressler has been associate editor-in-chief for IEEE Trans. on Mobile Computing and Elsevier Computer Communications as well as an editor for journals such as IEEE/ACM Trans. on Networking, IEEE Trans. on Network Science and Engineering, Elsevier Ad Hoc Networks, and Elsevier Nano Communication Networks. He has been chairing conferences such as IEEE INFOCOM, ACM MobiSys, ACM MobiHoc, IEEE VNC, IEEE GLOBECOM. He authored the textbooks Self-Organization in Sensor and Actor Networks published by Wiley & Sons and Vehicular Networking published by Cambridge University Press. He has been an IEEE Distinguished Lecturer as well as an ACM Distinguished Speaker. Dr. Dressler is an IEEE Fellow as well as an ACM Distinguished Member. He is a member of the German National Academy of Science and Engineering (acatech). He has been serving on the IEEE COMSOC Conference Council and the ACM SIGMOBILE Executive Committee. His research objectives include adaptive wireless networking (sub-6GHz, mmWave, visible light, molecular communication) and wireless-based sensing with applications in ad hoc and sensor networks, the Internet of Things, and Cyber-Physical Systems.


We will discuss the challenges and opportunities of distributed data management solutions ranging from the mobile edge to the data centers. Modern 5G networks promise to provide all means for communication in this domain, particularly when integrating Mobile Edge Computing (MEC). However, it turns out that despite the many advantages, it is unlikely that such services will be provided with sufficient coverage. As a novel concept, virtualized edge computing (V-Edge) have been proposed that bridges this gap. We present a learning-based approach to make such an V-Edge resilient to dynamics, failures, and even malicious attacks. In particular, we contrast centralized and federated learning approaches and reinforcement based approaches.

Xiuzhen Cheng, Shandong University

Meta Computing


Xiuzhen Cheng is now a Chair Professor, serving as the Dean of the School of Computer Science and Technology at Shandong University, Director of the Institute of Meta Computing at Shandong University, and Director of the DataChain Engineering Research Center of the Ministry of Education of China. Prof. Cheng is recognized as a top-level overseas talent by the Chinese government and is a Fellow of IEEE, CSEE, and AAIA. She serves as the Chief Scientist of a national key R&D program and is the founder of the CCF-accredited international conference WASA. She is also the founding Editor-in-Chief of the international journal "High-Confidence Computing." Prof. Cheng is an executive member of the CAAI Human-Machine Integration Intelligence Committee and serves as an expert for key projects and talent evaluation of the National Natural Science Foundation of China. She is on the editorial boards of top international journals such as IEEE TC and IEEE TWC. She previously worked as a Program Director with the US NSF (National Science Foundation of the US) and has served on NSF panelists for Computing and Communication Foundations (CCF) and Computer and Network Systems (CNS) divisions. Her research work has been widely cited, with a total Google Scholar citation exceeding 19,000 and a H-Index of 67.

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