Precision at heart: An IoT-based vertical federated learning approach for heterogeneous data-driven cardiovascular...

Cardiovascular disease (CVD) seriously threatens individual health, highlighting the importance of early detection and proactive mitigation. With advances in consumer electronics such as wearables and IoT, an opportunity exists to enhance CVD prediction for users. Machine learning (ML) has been widely used for predicting CVD risk (high/low) based on various factors and is a critical area of healthcare research. However, sharing data needed to predict CVD with machine learning models is challenging due to privacy concerns. Federated learning (FL) enables distributed training of ML models without sharing raw data. However, it requires that all training features be available to all clients.

To address this problem, we propose a method based on vertical federated learning (VFL) designed for consumer electronics platforms. The proposed method trains the Neural Network (NN) model in a distributed manner in which different parties hold different data features. In this work, each party maintains a portion of separate data features, performs calculations on them locally, and then transfers only the necessary information to train a NN model jointly. We employ the proposed method for different use cases where the dataset features are distributed between: (i) the patient and the hospital (2-splits); (ii) the patient, the doctor, and the laboratory (3-splits); and (iii) the patient, the doctor, the Electrocardiogram (ECG) center, and the laboratory (4-splits).

Using a realistic dataset publicly available, we test the proposed methodology, which gives around 90% accuracy, precision, recall, and f-score. It also does not need clients to possess the same features as compared to traditional Federated Learning.

This paper has an impact on the healthcare sector, where user data privacy is of utmost concern. This advancement will improve the ability of the healthcare sector to diagnose various diseases and contribute to the field of AI by improving distributed AI algorithms.