Transformer architectures are highly expressive because they use self-attention
mechanisms to encode long-range dependencies in the input sequences. In this paper, we …

Heterogeneity across clients in federated learning (FL) usually hinders the optimization
convergence and generalization performance when the aggregation of clients' knowledge …

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal behavior …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Anomaly detection on graphs plays a significant role in various domains, including
cybersecurity, e-commerce, and financial fraud detection. However, existing methods on …

In the context of big data, if the task of multivariate time series data anomaly detection cannot
be performed efficiently and accurately, it will bring great security risks to industrial systems …

Abstract Space anomaly detection plays a critical role in safeguarding the integrity and
reliability of space systems amid the rising tide of threats. This survey aims to deepen …

Graph transformers are a recent advancement in machine learning, offering a new class of
neural network models for graph-structured data. The synergy between transformers and …

Node classification is a substantial problem in graph-based fraud detection. Many existing
works adopt Graph Neural Networks (GNNs) to enhance fraud detectors. While promising …