Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm
which narrows the application scenarios of FL and decreases the enthusiasm of data …

Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly
developing in an era where privacy protection is increasingly valued. It is this rapid …

Privacy and security concerns in real-world applications have led to the development of
adversarially robust federated models. However, the straightforward combination between …

In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine
learning technique. However, most existing FL studies assume that the data distribution …

Federated learning is a distributed paradigm that allows multiple parties to collaboratively
train deep learning models without direct exchange of raw data. Nevertheless, the inherent …

Federated learning, as a privacy-preserving learning paradigm, restricts the access to data
of each local client, for protecting the privacy of the parties. However, in the case of …

Federated learning (FL) is a promising machine learning paradigm that collaborates with
client models to capture global knowledge. However, deploying FL models in real-world …

Unsupervised out-of-distribution (U-OOD) detection is to identify OOD data samples with a
detector trained solely on unlabeled in-distribution (ID) data. The likelihood function …

The ability to detect out-of-distribution (OOD) inputs is critical to guarantee the reliability of
classification models deployed in an open environment. A fundamental challenge in OOD …

In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine
learning technique. However, most existing FL studies assume that the data distribution …