Data privacy and data security are the main concerns in the digital era. The 3.5% centralized
increase in annual digital data and the use of machine learning and deep learning …

Federated Learning (FL) enables local model training on devices while collaboratively
updating a global model on a server, ensuring user data privacy by keeping it on the device …

Abstract Contemporary Machine Learning approaches (eg, Deep Learning) need huge
volumes of data to build accurate and robust statistical models. Nowadays, such data are …

The integration of wearable devices, IoT, and mobile internet technology has led to the
development of smart healthcare, which enables dynamic access to information …

Machine learning (ML) has recently been adopted in vehicular networks for applications
such as autonomous driving, road safety prediction and vehicular object detection, due to its …

Abstract Background and Objective: Contemporary Machine Learning approaches (eg,
Deep Learning) need huge volumes of data to build accurate and robust statistical models …

A distributed machine learning technique called federated learning allows numerous
Internet of Things (IoT) edge devices to work together to train a model without sharing their …

Since the traffic conditions change over time, machine learning models that predict traffic
flows must be updated continuously and efficiently in smart public transportation. Federated …

The emerging advances in personal devices and privacy concerns have given the rise to the
concept of Federated Learning. Federated Learning proves its effectiveness and privacy …

With the development of intelligent transportation system, it is urgent to transmit and analyze
traffic data based on Internet of Vehicles. Federated learning has the characteristics of …