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Paulo Nunes de Abreu, Architecting Collaboration, United Kingdom, United Kingdom
From 5G to 6G: Federated AI Security at the Edge of Healthcare’s Digital Infrastructure
Victor Chang, Aston Business School, Aston University, United Kingdom, United Kingdom
Short Bio
Victor Chang is a Professor of Applied AI and Business Analytics at Aston University, Birmingham, UK. Over a career spanning 26 years, he has worked at the intersection of applied machine learning, cybersecurity, and large-scale distributed systems, securing over £14 million in competitive research funding from UK and international sources. He is an internationally recognized authority in applied artificial intelligence, data science, cybersecurity, and cloud computing. Over his 26-year career bridging industry and academia, he has published over 300 peer-reviewed papers (accumulating over 31,000 citations, placing him in the top 0.2% of global scientists) and has secured over £3 million in research funding as Principal Investigator. Prof Chang founded 5 international conferences. Currently, 2 of them are still active working with INSTICC, IoTBDS and FEMIB. He has received a special INSTICC Ten-Year Service Award.
Professor Chang was named the Cybersecurity Professional of the Year 2026 by the global Cyber Security Awards, Data Scientist of the Year 2026 by UK and Computing’s AI and Software Development Awards and was selected for the Computing AI Leadership Index 2026 as the only academic among the UK's top 25 senior practitioners. He is also the recipient of the Data Leader of the Year 2025 (British Data Awards) and the Inspirational Individual of the Year 2024 (BCS UK IT Industry Awards). His pioneering, real-world implementations—such as the FedAvgVChang federated learning architecture, Deep-IFS intrusion detection systems and IoMT to collect and analyze data from different parts of the hospitals—focus on "Responsible Intelligence," ensuring that privacy-preserving AI and secure architectures seamlessly endure within critical environments like healthcare, finance, and public networks.
Abstract
Healthcare networks are under sustained attack, yet most hospital cybersecurity infrastructure was never designed for the distributed, software-defined environments that 6G is now bringing into view. Open RAN disaggregates the radio access network into multi-vendor, programmable components — genuine progress, but it multiplies attack surfaces in ways that centralised intrusion detection cannot adequately cover. Routing raw patient data to a central analysis node also creates a direct conflict with GDPR and NHS Digital standards. The architecture, in short, is working against itself.
Federated learning addresses this at a structural level. FedAvgVChang — an adaptive aggregation framework developed for heterogeneous, latency-sensitive clinical networks — trains models locally at each node and exchanges only encrypted parameter updates across a tiered edge–fog–cloud hierarchy. No patient data moves beyond its institutional boundary. Early results show 97% attack detection accuracy, a 55% reduction in bandwidth compared with centralised approaches, a 42% improvement in training convergence, and sub-second inference times for real-time threat response.
The talk sets this technical work in a broader context: how healthcare AI systems — not just the networks carrying them — need to be protected as 6G becomes operational. Drawing on the SecureAI4Public research programme, the keynote examines what the sector needs to address before the transition arrives, and what is already within reach.