Federated Learning with Smartphone Apps for Privacy-Preserving Chronic Disease Management and Cognitive Decline Detection in Seniors

The rapid aging of global populations has intensified the burden of chronic diseases such as diabetes, hypertension, and cardiovascular conditions, alongside the growing prevalence of cognitive decline including mild cognitive impairment and Alzheimer’s disease among seniors. Traditional healthcare monitoring systems rely on centralized data collection, which raises serious privacy concerns due to the sensitive nature of personal health information and potential breaches under regulations like GDPR and HIPAA. This paper proposes a novel federated learning (FL) framework seamlessly integrated with everyday smartphone applications to enable privacy-preserving chronic disease management and early cognitive decline detection in elderly users. The system leverages multi-modal data streams from smartphone sensors including accelerometers for gait analysis, microphones for speech pattern evaluation, cameras for facial expression monitoring, and usage logs for behavioural insights while performing all model training locally on user devices. A central server aggregates only encrypted model updates using algorithms like FedAvg, enhanced by differential privacy noise injection and secure multi-party computation to ensure raw data never leaves the phone. This decentralized paradigm addresses non-independent and identically distributed (non-IID) data challenges inherent in senior cohorts through personalized adaptation layers and communication-efficient techniques such as gradient quantization. Comprehensive experiments on synthetic datasets modelled after real-world benchmarks like UK Biobank and ADNI reveal the framework achieves 92% accuracy in chronic disease classification and 87% sensitivity in cognitive decline detection, outperforming centralized baselines by 15% in privacy-utility trade-offs while reducing data transmission by over 99%. Real-world deployment considerations, including battery optimization and user-centric interfaces with voice commands and large fonts, make this solution accessible for tech-novice seniors. By transforming ubiquitous smartphones into proactive, ethical health companions, our approach paves the way for scalable, longitudinal elderly care that empowers independence without compromising confidentiality, with implications for telehealth integration and community-based pilots.