In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …

Data augmentation techniques, such as simple image transformations and combinations,
are highly effective at improving the generalization of computer vision models, especially …

Vertical Federated Learning (VFL) is a federated learning paradigm where multiple
participants, who share the same set of samples but hold different features, jointly train …

Data scarcity is a significant obstacle hindering the learning of powerful machine learning
models in critical healthcare applications. Data-sharing mechanisms among multiple entities …

In recent years, split learning (SL) has emerged as a promising distributed learning
framework that can utilize Big Data in parallel without privacy leakage while reducing client …

Automatic modulation classification (AMC) is a technology that identifies a modulation
scheme without prior signal information and plays a vital role in various applications …