4 papers
BettiSplit: Topology-Guided Privacy-Aware Split Learning Against Feature Inversion and Gradient Leakage
Akarsh K. Nair, Muhammad Arifur Rahman, David Brown +1
Split learning enables collaborative model training by partitioning neural networks across clients and servers. However, improper split placement can lead to severe privacy leakage…
Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction
Ruth Amey, Muhammad Arifur Rahman, Taha Osman +4
Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalised…
SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework
Akarsh K Nair, Muhammad Arifur Rahman, Nicholas Shopland +8
Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by restricted data sharing, client heterogene…
A Platform-Agnostic Multimodal Digital Human Modelling Framework: Neurophysiological Sensing in Game-Based Interaction
Daniel J. Buxton, Mufti Mahmud, Jordan J. Bird +2
Digital Human Modelling (DHM) is increasingly shaped by advances in AI, wearable biosensing, and interactive digital environments, particularly in research addressing accessibility…