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 …

In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Although machine learning (ML) has shown promise across disciplines, out-of-sample
generalizability is concerning. This is currently addressed by sharing multi-site data, but …

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 …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Benefiting from the ability to process and integrate data from various modalities, multi-modal
foundation models (FMs) facilitate potential applications across a range of fields, including …

In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …

As a crucial role in the prognostic and health management of mechanical equipment, fault
diagnosis encounters serious challenges, such as the scarcity of fault samples, the high cost …

The field of oncology has witnessed an extraordinary surge in the application of big data and
artificial intelligence (AI). AI development has made multiscale and multimodal data fusion …

Federated learning (FL) enables multiple clients to collaboratively train machine learning
models without revealing their private training data. In conventional FL, the system follows …