Reinforcement Learning-Driven Adaptive Aggregation for Blockchain-Enabled Federated Learning in Secure EHR Management

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.

@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},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Cai Yanmin and Wang Lei and Zainura Idrus and Jasni Mohamad Zain and Marina Yusoff},
  doi       = {10.14569/IJACSA.2025.0161271},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161271}
}