Chair of the IEEE IoT Educational Activities Committee and Award Presenter, Dr Wanqing Tu, alongside Di Wu

From St Andrews to Chengdu, Dr. Di Wu has been awarded third place in the 2025 IEEE World Forum on Internet of Things PhD Thesis Competition.  IEEE is an internationally recognised organisation within electrical and electronics engineering. By participating in such an event, participants can receive valuable external feedback and connect with a larger community focused on the future vision of IoT Systems.

With a thesis titled “Distributed Machine Learning on Edge Computing Systems,” Dr. Wu proposes three techniques to better train machine learning models that directly affect small devices such as sensors, smartphones, and every day IoT gadgets. He states that the focus on smaller devices is becoming even more important due to the grand size of modern datasets, as well as how time-consuming, expensive, and at-risk to user privacy sending information to the cloud can be:

In my research, I proposed three techniques to make this kind of training more practical. The first helps devices decide how to split and share the workload. The second reduces the amount of data that needs to be exchanged during training. And the third lowers the amount of computation each device has to perform. Finally, I brought all these ideas together into one complete system. When we tested it on real IoT devices, it trained models faster, communicated less data, and achieved better accuracy compared with existing methods.

This improvement in efficiency suitably aligns with IEEE’S 2025 theme of “Smart and Sustainable IoT.” ‘To me’ Dr Wu states, ‘“smart” IoT means giving devices the ability to learn and make decisions locally. While “sustainable” IoT means doing this in a way that saves energy, protects user privacy, and can scale as the number of devices continues to grow. Therefore, by cutting down the computation and communication needed for training, intelligent IoT systems can become more sustainable and easier to deploy in practice.’ With this in mind, Dr. Wu propelled forward with his research that was also greatly influenced by the challenges he experienced as a machine learning engineer and the specific research questions that arose from reading subject-specific literature, discussing ideas with his supervisor Blesson Varghese, as well as building real-world prototypes throughout his PhD journey.

I truly see preparing for the nomination as a natural step that came out of the work I did during my PhD. I had published papers in related venues, including the IEEE Internet of Things Journal and IEEE Transactions on Parallel and Distributed Systems, which gave me some confidence that my work was heading in the right direction. Furthermore, writing my thesis, presenting ideas at conferences, as well as preparing for my viva helped me clarify my ideas which eventually helped me piece together and highlight the parts of my research that were most relevant to the theme. I would really encourage PhD graduates to apply for these kinds of thesis competitions.[1]

Now working as a Research Fellow funded by the UK National Edge AI Hub, Dr. Wu reflects on how this year’s IEEE displayed active research engagement with the intersection of AI and IoT — ‘both AI for IoT, where AI is used to solve IoT-specific problems, and AI on IoT, where we try to bring AI capabilities directly onto IoT devices.’ Another emerging direction he noted was the integration of sensing, communication, and computation. ‘These used to be relatively separate research areas, each led by different communities. But now we’re seeing growing interest in combining them into a single, unified system, which I think has a lot of potential.’ As Dr. Wu continues to explore efficient and scalable machine learning systems at the edge, he believes his new research direction will move beyond traditional federated learning, turning specifically to how agent-based systems and efficient foundation models (such as large language models) can be brought to the edge. ‘These areas are quite different from conventional ML systems, but they open up exciting possibilities for the next generation of edge intelligence,’ he concludes.

[1] Dr. Di Wu personally recommends competitions such as, ACM PhD Competition, the IEEE IoT PhD Competition, the IEEE TCSC PhD Thesis Award, as well as local competitions like the SICSA PhD Competition in Scotland.