Over the past years, the increasingly severe data island problem has spawned an emerging
distributed deep learning framework—federated learning, in which the global model can be …

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), as a distributed machine learning setting, has received
considerable attention in recent years. To alleviate privacy concerns, FL essentially …

As an emerging training model with neural networks, federated learning has received
widespread attention due to its ability to update parameters without collecting users' raw …

Federated learning (FL) protects training data in clients by collaboratively training local
machine learning models of clients for a global model, instead of directly feeding the training …

Federated learning (FL) has emerged as an ideal privacy-preserving learning technique
which can train a global model in a collaborative way while preserving the private data in the …

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 …

The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

Federated learning (FL) enables multiple worker devices share local models trained on their
private data to collaboratively train a machine learning model. However, local models are …

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