Accurate prediction of bearing remaining useful life (RUL) is essential for machine health
management. In existing data-driven prognostic methods, centralized data resources and …

With increasing concern about user data privacy, federated learning (FL) has been
developed as a unique training paradigm for training machine learning models on edge …

Federated Learning (FL) has emerged as a powerful approach that enables collaborative
distributed model training without the need for data sharing. However, FL grapples with …

Concerned with user data privacy, this paper presents a new federated learning (FL) method
that trains machine learning models on edge devices without accessing sensitive data …

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 …

In response to the increasing volume and sensitivity of data, traditional centralized
computing models face challenges, such as data security breaches and regulatory hurdles …

In the realm of federated learning (FL), the exchange of model data may inadvertently
expose sensitive information of participants, leading to significant privacy concerns. Existing …

Researchers have resorted to model quantization to compress and accelerate graph neural
networks (GNNs). Nevertheless, several challenges remain:(1) quantization functions …

There are several unsolved problems in federated learning, such as the security concerns
and communication costs associated with it. Differential privacy (DP) offers effective privacy …

Edge machine learning (ML) enables localized processing of data on devices and is
underpinned by deep neural networks (DNNs). However, DNNs cannot be easily run on …