Since traditional federated learning (FL) algorithms cannot provide sufficient privacy
guarantees, an increasing number of approaches apply local differential privacy (LDP) …

Federated learning (FL) is a distributed machine learning method that effectively protects
personal data. Many studies on federated learning assumed that all clients have consistent …

Privacy preserving is a severe challenge in machine learning and artificial intelligence.
Recently, many works have been devoted to solving this problem by proposing various …

Train machine learning models on sensitive user data has raised increasing privacy
concerns in many areas. Federated learning is a popular approach for privacy protection …

Local differential privacy federated learning (LDP-FL) is a framework to achieve high local
data privacy protection while training the model in a decentralized environment. Currently …

In recent years, federated learning has rapidly become a new research hotspot in the field of
secure machine learning. However, unprotected traditional federated learning can easily …

As a popular machine learning method, federated learning (FL) can effectively solve the
issues of data silos and data privacy. However, traditional federated learning schemes …

Under the needs of processing huge amounts of data, providing high-quality service, and
protecting user privacy in artificial intelligence of things (AIoT), federated learning (FL) has …

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 …

The Internet of Things (IoT) is penetrating many aspects of our daily life with the proliferation
of artificial intelligence applications. Federated learning (FL) has emerged as a promising …