Abstract: With the rapid digitization of healthcare, blockchain-integrated federated learning (FL) for EHR management faces challenges of heterogeneous data, high latency, and adversarial vulnerabilities. This study proposes a novel Reinforcement Learning-Driven Adaptive Aggregation (RL-DAA) in an enhanced blockchain-FL framework, using Q-learning to dynamically optimize model weights based on trust, data quality, and node reliability. RL-DAA reduces computational overhead by 40% via state-action-reward optimization (mitigating non-IID bias) and boosts robustness against Byzantine faults by 35% with fault-tolerant rewards. Validated on adapted CIFAR-10 and real-world healthcare simulations, compared to EPP-BCFL and baseline models, RL-DAA achieves 96.5% accuracy, 45% lower latency, and 38% reduced energy consumption. By dynamically balancing efficiency, privacy, and robustness via RL-driven optimization, this work advances secure, scalable EHR management, with broader potential in privacy-sensitive domains.

Cai Yanmin, Wang Lei, Zainura Idrus, Jasni Mohamad Zain and Marina Yusoff. “Reinforcement Learning-Driven Adaptive Aggregation for Blockchain-Enabled Federated Learning in Secure EHR Management”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161271

@article{Yanmin2025,
title = {Reinforcement Learning-Driven Adaptive Aggregation for Blockchain-Enabled Federated Learning in Secure EHR Management},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161271},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161271},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Cai Yanmin and Wang Lei and Zainura Idrus and Jasni Mohamad Zain and Marina Yusoff}
}