Massive data collection in modern systems has paved the way for data-driven machine
learning, a promising technique for creating reliable and robust statistical models. By …

Federated learning is a machine learning approach where multiple edge devices, each
holding local data samples, send a locally trained model to the central server, and the …

Training machine learning models on wearable sensor data is useful for applications like
human activity recognition (HAR), but the sensitivity of such data often precludes centralized …

Federated learning was developed as a distributed learning paradigm to train machine
learning models using data collected by individual devices without uploading it to a central …

Traditional healthcare systems utilize centralized approaches for building machine-learning
models for disease diagnosis. It requires sharing raw data to the centralized server, which is …

Federated Learning (FL) is a distributed technique that allows multiple users to train models
collaboratively without accessing private and sensitive data. Iteratively, each user trains a …

In the late 20 th century, Machine Learning underwent a paradigm shift from model-driven to
data-driven design. Rather than field specific models, advances in sensors, data storage …

Massive data collection in modern systems has paved the way for data-driven machine
learning, a promising technique for creating reliable and robust statistical models. By …