Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …

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

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 …

Abstract Spatial Data Intelligence (SDI) encompasses acquiring, storing, analyzing, mining,
and visualizing spatial data to gain insights into the physical world and uncover valuable …

Federated learning (FL) offers an effective privacy protection mechanism for cross-center
medical collaboration and data sharing. In multi-site medical image segmentation, FL allows …

The distinct attributes of Internet of Things (IoT) devices, including the disparity between
training and testing data distributions and limited availability of training data, pose …

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G)
networks due to dense connectivity, ultrareliability, low latency, and high throughput …

We introduce a novel strategy to train randomised predictors in federated learning, where
each node of the network aims at preserving its privacy by releasing a local predictor but …

Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly
developing in an era where privacy protection is increasingly valued. It is this rapid …

Federated learning is a popular machine learning paradigm that achieves decentralized
model training on distributed devices, ensuring data decentralization, privacy protection, and …