Anuja Nair

Abstract Transformer models have demonstrated remarkable success in natural language processing (NLP) tasks, but their deployment in distributed environments faces critical challenges including high computational demands, large memory footprints, and privacy concerns when handling sensitive data. Existing federated learning (FL) implementations with transformers often sacrifice either model performance for privacy or resource efficiency for accuracy.

Abstract Parkinson’s disease is a progressive neurodegenerative disorder that primarily affects the motor system and is often difficult to diagnose in its early stages due to subtle and age-overlapping symptoms. To address this challenge, we propose a hybrid deep learning-based diagnostic framework using T2-weighted MRI scans from the PPMI dataset consisting of 77 healthy control (HC) and 223 Parkinson’s disease (PD) subjects.