Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Nowadays, with the development of artificial intelligence (AI), privacy issues attract wide
attention from society and individuals. It is desirable to make the data available but invisible …

To defend the inference attacks and mitigate the sensitive information leakages in Federated
Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy …

Abstract Model heterogeneous federated learning is a realistic and challenging problem.
However, due to the limitations of data collection, storage, and transmission conditions, as …

Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …

While preserving the privacy of federated learning (FL), differential privacy (DP) inevitably
degrades the utility (ie, accuracy) of FL due to model perturbations caused by DP noise …

The metaverse is a nascent concept that envisions a virtual universe, a collaborative space
where individuals can interact, create, and participate in a wide range of activities. Privacy in …

Although federated learning can provide privacy protection for individual raw data, some
studies have shown that the shared parameters or gradients under federated learning may …

Federated learning (FL) is a hot collaborative training framework via aggregating model
parameters of decentralized local clients. However, most FL methods unreasonably assume …

Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …