The label noise is a serious problem limiting the performance of federated learning.
According to the performance evaluation for the trained federated models, data selection …

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 …

Abstract In recent years, Deep Neural Networks (DNN) have emerged as a practical method
for image recognition. The raw data, which contain sensitive information, are generally …

Rapid advances in sensing technology have enabled the collection of vast amounts of data
from manufacturing operations, which has expedited big-data-driven innovations in …

Local differential privacy (LDP) is a state-of-the-art privacy notion that enables terminal
participants to share their private data safely while controlling the privacy disclosure at the …

In recent years, with the escalating security demands of the Internet of Vehicles (IoV),
concerns over safety have intensified. To prevent security incidents and privacy breaches …

Aiming at the problem of adopting the same level of privacy protection for sensitive data in
the process of data collection and ignoring the difference in privacy protection requirements …

Consensus is one of the most important topics in distributed multi-agent systems (MAS). In
general, existing consensus approaches aim at driving agents to reach an agreement via …

We initiate the study of locally differentially private (LDP) learning with public features. We
define semi-feature LDP, where some features are publicly available while the remaining …

With the continuous promotion and deepened application of Machine Learning-as-a-Service
(MLaaS) across various societal domains, its privacy problems occur frequently and receive …