3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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…