Privacy-Preserving Personalized Seizure Risk Monitoring Using Reinforcement Learning and Federated Learning on Wearable Data
Abstract
Seizure risk monitoring with wearable physiological data is difficult due to temporal uncertainty, patient heterogeneity, class imbalance, and severe privacy restrictions. Existing techniques frequently rely on static supervised learning or intrusive EEG-based monitoring, which limits their usefulness for long-term, tailored, and privacy-preserving deployment. This thesis presents seizure risk monitoring as a sequential decision-making issue with reinforcement learning, allowing for temporally consistent and prevention-oriented choice policies. A tailored framework is created using a Proximal Policy Optimization (PPO) agent and a Transformer-based temporal encoder, and it is tested with extensive ablation studies on wearable-inspired physiological signals under severely unbalanced settings. The findings show that reward-driven learning and temporal consistency give more relevant evaluation than point-wise accuracy in safety-critical monitoring activities. The framework is further expanded to include a federated reinforcement learning context, which allows for collaborative learning across patients without exchanging raw data. To address instability caused by non-IID data and weak-signal patients, we offer a trust-aware aggregation technique that dynamically reweights patient contributions based on risk-state detection performance and reward alignment. This technique increases robustness and encourages balanced conduct among diverse patients. Overall, this study proposes a…
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.