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 …

Graph neural networks (GNNs) have attracted extensive research attention in recent years
due to their capability to progress with graph data and have been widely used in practical …

Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …

Vertical Federated Learning (VFL) enables multiple data owners, each holding a different
subset of features about largely overlapping sets of data sample (s), to jointly train a useful …

Abstract Fairness issues in Deep Learning models have recently received increasing
attention due to their significant societal impact. Although methods for mitigating unfairness …

Federated Learning (FL) enables multiple partici-pants to collaboratively train a model in a
privacy-preserving way. The performance of the FL model heavily depends on the quality of …

Insufficient data is a long-standing challenge for Brain–Computer Interface (BCI) to build a
high-performance deep learning model. Though numerous research groups and institutes …

Abstract Although Federated Learning (FL) enables collaborative model training without
sharing the raw data of clients, it encounters low-performance problems caused by various …

As a promising distributed machine learning paradigm, Federated Learning (FL) has
attracted increasing attention to deal with data silo problems without compromising user …

Federated learning (FL) enables multiple data owners to build machine learning models
collaboratively without exposing their private local data. In order for FL to achieve …