Federated learning (FL) unleashes the full potential of training a global statistical model
collaboratively from edge clients. In wireless FL, for the scarcity of spectrum, only a fraction …

Social media networks now make it easy to access, in real-time, massive amounts of
information from all over the world. They are often the primary source of information for …

The credit risk assessment process is necessary for maintaining financial stability, cost and
time efficiency, model performance accuracy, comparability analysis and future business …

Vertical federated learning (VFL) is a distributed machine learning paradigm that
collaboratively trains models using passive parties with features and an active party with …

Enhancing our comprehension of soil processes and their impact on Earth requires precise
quantification of human-induced soil alterations, particularly those related to land use/cover …

Using the Internet of Things (IoT) for various applications, such as home and wearables
devices, network applications, and even self-driven vehicles, detecting abnormal traffic is …

Abstract Mathematics and statistics have a significant impact on the advancement of most
trending sciences like machine learning, artificial intelligence, and data science. In this …

As a privacy-preserving method for implementing Vertical Federated Learning, Split
Learning has been extensively researched. However, numerous studies have indicated that …

Highly accurate detection of pulsars is mandatory. With the application of machine learning
(ML) algorithms, the detection of pulsars can certainly be improved if the dataset is …

Predicting click-through rates (CTR) is essential for optimizing the effectiveness of mobile
advertising campaigns, where accurate prediction of user interactions can significantly …