Federated deep learning has been widely used in various fields. To protect data privacy,
many privacy-preservingapproaches have been designed and implemented in various …

Privacy-preserving machine learning allows multiple parties to perform distributed data
analytics while guaranteeing individual privacy. In this area, researchers have proposed …

Deep learning has been applied in many areas, such as computer vision, natural language
processing and emotion analysis. Differing from the traditional deep learning that collects …

With the advance of machine learning technology and especially the explosive growth of big
data, federated learning, which allows multiple participants to jointly train a high-quality …

Federated learning is a recently proposed paradigm that presents significant advantages in
privacy-preserving machine learning services. It enables the deep learning applications on …

Abstract Machine learning models trained on sensitive real-world data promise
improvements to everything from medical screening to disease outbreak discovery. In many …

Due to the complexity of the data environment, many organizations prefer to train deep
learning models together by sharing training sets. However, this process is always …

Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …

Decentralized federated learning (DFL) is an emerging privacy-preserving machine learning
framework, where multiple data owners cooperate to train a global model without any …

Federated learning (FL) has attracted growing interest for enabling privacy-preserving
machine learning on data stored at multiple users while avoiding moving the data off-device …