Federated learning has emerged as an effective paradigm to achieve privacy-preserving
collaborative learning among different parties. Compared to traditional centralized learning …

In the past decades, artificial intelligence (AI) has achieved unprecedented success, where
statistical models become the central entity in AI. However, the centralized training and …

Abstract Model heterogeneous federated learning is a realistic and challenging problem.
However, due to the limitations of data collection, storage, and transmission conditions, as …

While federated learning (FL) is a well-known privacy-preserving (PP) solution, recent
studies demonstrate that it still has privacy problems and vulnerabilities, particularly in the …

The growing application of machine learning (ML) techniques in healthcare has led to
increased interest in federated learning (FL), which enables the secure and private training …

Federated Learning (FL) has recently emerged as a promising distributed machine learning
framework to preserve clients' privacy, by allowing multiple clients to upload the gradients …

Federated learning is an important framework in modern machine learning that seeks to
integrate the training of learning models from multiple users, each user having their own …

Federated Learning (FL) is a machine learning technique in which collaborative and
distributed learning is performed, while the private data reside locally on the client. Rather …

Artificial intelligence generated content (AIGC) relies on advanced AI algorithms supported
by extensive datasets and substantial computing power to generate precise and pertinent …

Gradient inversion attacks (GIAs) have posed significant challenges to the emerging
paradigm of distributed learning, which aims to reconstruct the private training data of clients …