Federated learning (FL) is a secure distributed machine learning paradigm that addresses
the issue of data silos in building a joint model. Its unique distributed training mode and the …

Federated learning enables data owners to jointly train a neural network without sharing
their personal data, which makes it possible to share sensitive data generated from various …

Federated learning (FL) has achieved state-of-the-art performance in distributed learning
tasks with privacy requirements. However, it has been discovered that FL is vulnerable to …

Federated learning has gained extensive interest in recent years owing to its ability to
update model parameters without obtaining raw data from users, which makes it a viable …

With the continuous development of deep learning in the field of image generation models, a
large number of vivid forged faces have been generated and spread on the Internet. These …

In federated learning (FL), each client collaboratively trains the global model through the
cloud server (CS) without sharing its original dataset in edge computing. However, CS can …

In vehicular ad-hoc networks (VANET), federated learning enables vehicles to
collaboratively train a global model for intelligent transportation without sharing their local …

The widespread adoption of deep learning is facilitated in part by the availability of large-
scale data for training desirable models. However, these data may involve sensitive …

Web3 has received a lot of attention since its emergence. It aims to provide users with more
diverse and vivid web services as well as the complete control over their own data. To …

Federated Learning (FL) enables multiple distributed clients to collaboratively train a model
with owned datasets. To avoid the potential privacy threat in FL, researchers propose the DP …