# Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI

arXiv: https://arxiv.org/abs/2105.12497 · HTML: https://mlsanity.com/paper/2105.12497/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 83 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://mlsanity.com/paper/1810.04020/) (2018)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [When Machine Learning Meets Privacy: A Survey and Outlook](https://mlsanity.com/paper/2011.11819/) (2020)
- [Differential Privacy and Machine Learning: a Survey and Review](https://mlsanity.com/paper/1412.7584/) (2014)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [Visual Analytics for Explainable Deep Learning](https://mlsanity.com/paper/1804.02527/) (2018)
- [HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks](https://mlsanity.com/paper/2002.05262/) (2020)
- [Cross-position Activity Recognition with Stratified Transfer Learning](https://mlsanity.com/paper/1806.09776/) (2018)
- [Recognizing Abnormal Heart Sounds Using Deep Learning](https://mlsanity.com/paper/1707.04642/) (2017)

## Built on by (most cited first, 26 of 26)

- [Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction](https://mlsanity.com/paper/2409.00265/) (2024)
- [From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape](https://mlsanity.com/paper/2312.10868/) (2023)
- [Remote patient monitoring using artificial intelligence: Current state, applications, and challenges](https://mlsanity.com/paper/2301.10009/) (2023)
- [LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification](https://mlsanity.com/paper/2207.12381/) (2022)
- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals](https://mlsanity.com/paper/2208.10993/) (2022)
- [Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine](https://mlsanity.com/paper/2211.09317/) (2022)
- [Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review](https://mlsanity.com/paper/2411.05874/) (2024)
- [Federated Learning: Issues in Medical Application](https://mlsanity.com/paper/2109.00202/) (2021)
- [Painting the black box white: experimental findings from applying XAI to an ECG reading setting](https://mlsanity.com/paper/2210.15236/) (2022)
- [Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosis](https://mlsanity.com/paper/2302.01241/) (2023)
- [A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing](https://mlsanity.com/paper/2303.13727/) (2023)
- [ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning](https://mlsanity.com/paper/2304.04646/) (2023)
- [Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data](https://mlsanity.com/paper/2405.00725/) (2024)
- [Federated and Transfer Learning for Cancer Detection Based on Image Analysis](https://mlsanity.com/paper/2405.20126/) (2024)
- [Cross-Silo Heterogeneous Model Federated Multitask Learning](https://mlsanity.com/paper/2202.08603/) (2022)
- [Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence](https://mlsanity.com/paper/2312.01555/) (2023)
- [A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility](https://mlsanity.com/paper/2503.07276/) (2025)
- [ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification](https://mlsanity.com/paper/2505.03787/) (2025)
- [Parameter Averaging for Feature Ranking](https://mlsanity.com/paper/2208.03249/) (2022)
- [On the Importance of Sign Labeling: The Hamburg Sign Language Notation System Case Study](https://mlsanity.com/paper/2302.10768/) (2023)
- [GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning](https://mlsanity.com/paper/2410.08634/) (2024)
- [Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks](https://mlsanity.com/paper/2505.14659/) (2025)
- [Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices](https://mlsanity.com/paper/2606.11556/) (2026)
- [Federated Explainable Artificial Intelligence: Roles, Architectures, Evaluation, and Open Challenges](https://mlsanity.com/paper/2607.13045/) (2026)
- [Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning](https://mlsanity.com/paper/2608.13914/) (2026)

## Abstract (arXiv)

Deep learning play a vital role in classifying different arrhythmias using the electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and it can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning models are like black-box, with no explainability of the predicted results, which is often required in clinical healthcare. This limits the application of deep learning in real-world health systems. In this paper, we design a new explainable artificial intelligence (XAI) based deep learning framework in a federated setting for ECG-based healthcare applications. The federated setting is used to solve issues such as data availability and privacy concerns. Furthermore, the proposed framework setting effectively classifies arrhythmia's using an autoencoder and a classifier, both based on a convolutional neural network (CNN). Additionally, we propose an XAI-based module on top of the proposed classifier to explain the classification results, which help clinical practitioners make quick and reliable decisions. The proposed framework was trained and tested using the MIT-BIH Arrhythmia database. The classifier achieved accuracy up to 94% and 98% for arrhythmia detection using noisy and clean data, respectively, with five-fold cross-validation.