Welcome to 2024 8th International Conference on Big Data and Internet of Things (BDIOT2024)
∷
∷
∷
Biosketch: Simon James Fong graduated from La Trobe University, Australia, with a 1st Class Honours BEng. Computer Systems degree and a PhD. Computer Science degree in 1993 and 1998 respectively. Simon is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau. Dr. Fong has published over 500 500 international conference and peer-reviewed journal papers, mostly in the areas of data mining and AI medical applications. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier, IEEE IT Professional Magazine, and various special issues of SCIE-indexed journals.
Title of Speech: Federated Learning, Big Data, and IoT Synergy for e-Health Transformation: A Global Collaborative Framework for Scalable, Sustainable, and AI-Driven Healthcare Diagnosis
Abstract: The integration of big data, AI, machine learning, and IoT health sensing applications is set to revolutionize healthcare by enabling scalable, real-time, and AI-powered diagnostic systems. In this invited speech, we would present an innovative, cloud-based framework that leverages federated learning to foster global collaboration among clinicians, researchers, and institutions. The framework facilitates decentralized AI model training across diverse healthcare centers while preserving data privacy, allowing the secure sharing of insights through a worldwide consortium. It empowers next-generation e-health diagnostics by combining data from distributed sources, neurocognitive experimentation, and IoT health sensors, enabling continuous model growth and improvements in real time. By connecting stakeholders across different geographical regions, the platform creates an ecosystem where global collaboration drives sustainable advancements in healthcare. Case studies such as glomerulonephritis diagnosis illustrate the model's capacity for personalized, adaptive, and accurate healthcare predictions, while IoT-driven health monitoring and neurocognitive experimentation broaden its application scope. As these collaborative networks evolve, the framework ensures diagnostic models remain accurate, scalable, and aligned with emerging innovations. This approach fosters a future where connected devices, shared knowledge, and AI-driven systems continually refine and optimize healthcare delivery worldwide. A real life case of Consortium of BRICS E-health Research Taskforces Association (COBERTA) will be presented.