The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE)
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …

Federated learning emerges as a solution to the dilemma of data silos while safeguarding
data privacy, particularly relevant in the consumer electronics sector where user data privacy …

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

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated Learning (FL) is a collaborative training paradigm whereby a global Machine
Learning (ML) model is trained using typically private and distributed data sources without …

Due to underlying privacy-sensitive information in user-item interaction data, the risk of
privacy leakage exists in the centralized-training recommender system (RecSys). To this …

The rising popularity of electric vehicles (EVs) underscores the potential of vehicle-to-grid
(V2G) technology to contribute to load peak-shaving, valley-filling, and photovoltaic (PV) self …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …