Attention-Driven Hierarchical Federated Learning for Privacy-Preserving Edge AI in Heterogeneous IoT Networks

ECG arrhythmia detection is very important in identification and management of patients with cardiac disorders. Centralized machine learning models are privacy invasive, and distributed ones poorly deal with the data heterogeneity of the devices. These challenges are responded to by presenting the edge AI an attention-driven hierarchical federated learning framework with 1-Dimensional Convolutional Neural Network (1D-CNN) - Long Short-Term Memory (LSTM) -Attention to classify arrhythmia in ECG recordings. This model includes the spatial characteristics of ECG signals and the temporal characteristics of attention maps, identifying the significant areas of the inputs and providing high interpretability and accuracy of the model. Thus, federated learning is applied to perform model training in a decentralized process through the Privacy-Preserving while the raw data remains on the edge devices. For assessment, this study utilized St. Petersburg INCART 12-lead Arrhythmia Database and Wearable Health Monitoring has given an overall classification accuracy of 96.5% with an average of AUC-ROC of 0.98 with five classes as Normal (N), Supraventricular (S), Ventricular (V), Fusion (F), and Unclassified (Q). The proposed model was created using the Python programming language with the TensorFlow framework deep learning and tested using Raspberry Pi devices to mimic edge settings. Overall, this study proves that it is possible to classify using IoT Device ECG arrhythmia reliably and securely on devices with limited resources, which will enable real-time cardiac monitoring.

@article{Pande2025,
  title     = {Attention-Driven Hierarchical Federated Learning for Privacy-Preserving Edge AI in Heterogeneous IoT Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Pournima Pande and Bukya Mohan Babu and Poonam Bhargav and T L Deepika Roy and Elangovan Muniyandy and Yousef A. Baker El-Ebiary and V Diana Earshia},
  doi       = {10.14569/IJACSA.2025.0160545},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160545}
}