Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …

The rapid development of Internet of Things (IoT) systems has led to the problem of
managing and analyzing the large volumes of data that they generate. Traditional …

Federated learning involves training statistical models over remote devices or siloed data
centers, such as mobile phones or hospitals, while keeping data localized. Training in …

User-generated data distributions are often imbalanced across devices and labels,
hampering the performance of federated learning (FL). To remedy to this non-independent …

The fast proliferation of edge computing devices brings an increasing growth of data, which
directly promotes machine learning (ML) technology development. However, privacy issues …

When the data is distributed across multiple servers, lowering the communication cost
between the servers (or workers) while solving the distributed learning problem is an …

Privacy and data security have become the new hot topic for regulators in recent years. As a
result, Federated Learning (FL)(also called collaborative learning) has emerged as a new …

By leveraging deep learning technologies, data-driven-based approaches have reached
great success with the rapid increase of data generated for medical applications. However …

As promising privacy-preserving machine learning technology, federated learning enables
multiple clients to train the joint global model via sharing model parameters. However …

Deep learning-based medical image analysis is an effective and precise method for
identifying various cancer types. However, due to concerns over patient privacy, sharing …