  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.
