Federated learning (FL) is an efficient decentralized machine learning methodology for
processing nonindependent and identically distributed (non-IID) data due to geographical …

Edge Computing (EC) has gained significant traction in recent years, promising enhanced
efficiency by integrating Artificial Intelligence (AI) capabilities at the edge. While the focus …

The emerging integration of Internet of Things (IoT) and AI has unlocked numerous
opportunities for innovation across diverse industries. However, growing privacy concerns …

Federated Learning (FL) promises to offer a major paradigm shift in the way deep learning
models are trained at scale, yet malicious clients can surreptitiously embed backdoors into …

With the wide adoption of SOA (Service Oriented Architecture) in software engineering, a
large number of Web services have emerged to meet the Mashup development …

Purpose With the increasing number of Web services, correct and efficient classification of
Web services is crucial to improve the efficiency of service discovery. However, existing Web …

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has
significantly impacted Web Service Classification, a critical task for service discovery …

Federated learning has become a popular machine learning paradigm with many potential
real-life applications, including recommendation systems, the Internet of Things (IoT) …

Text summarization is essential for information aggregation and demands large amounts of
training data. However, concerns about data privacy and security limit data collection and …

With the advance of service computing technology, the number of Web APIs has risen
dramatically over the Internet. Users tend to use Web APIs to achieve their business needs …