Agentic ElderFedLearn: A Differential Privacy-Based Approach for Elderly Disease Prediction

Alzheimer's disease (AD) is considered to be a significant health challenge that affects the cognitive ability of elderly people. The effects can only be slowed down if the disease is detected at an early stage. Researchers have extensively explored the use of machine learning algorithms to ensure early detection and prediction. However, effective models are complex, hence limiting their interpretability and privacy. Federated learning (FL) approaches have also been proposed to add privacy aspect to the machine learning models, however, FL methods are vulnerable to model related attacks. To address this we propose Agentic ElderFedLearn, a novel framework that proceeds in the following steps: 1) model healthcare institutions as autonomous artificial intelligence (AI) agents training local models on multimodal data [electronic health record (EHR) and synthetic magnetic resonance imaging (MRI)]; 2) apply personalized differential privacy (DP) to gradients, adapting budgets based on dataset size and sensitivity; 3) use multiagent reinforcement learning (MARL) to optimize agent interactions, such as privacy adjustments and communication; and 4) perform effective aggregation via weighted trimmed mean to defend against attacks. This innovation ensures privacy, handles heterogeneity, and achieves 94% accuracy with 0.93 F1-score, outperforming centralized approaches while using synthetic data.