Federated Learning (FL) offers innovative solutions for privacy-preserving collaborative
machine learning (ML). Despite its promising potential, FL is vulnerable to various attacks …

Federated Learning (FL) enables multiple devices to collaboratively train models without
sharing their raw data. Considering that clients may prefer to design their own models …

Label noise, an inevitable issue in various real-world datasets, tends to impair the
performance of deep neural networks. A large body of literature focuses on symmetric co …

The success of most federated learning (FL) methods heavily depends on label quality,
which is often inaccessible in real-world scenarios, such as medicine, leading to the …

Federated Graph Learning (FGL) is a distributed machine learning paradigm based on
graph neural networks, enabling secure and collaborative modeling of local graph data …

In actual scenarios, whether manually or automatically annotated, label noise is inevitably
generated in the training data, which can affect the effectiveness of deep CNN models. The …

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …

Contrary to centralized federated learning (CFL), decentralized federated learning (DFL)
allows clients to cooperate in training their local models without relying on a central …