Federated learning (FL), as a distributed machine learning setting, has received
considerable attention in recent years. To alleviate privacy concerns, FL essentially …

Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning
paradigm that aggregates user-trained local gradients into a federated model through a …

Federated learning (FL) has emerged as an ideal privacy-preserving learning technique
which can train a global model in a collaborative way while preserving the private data in the …

Although federated learning offers a level of privacy by aggregating user data without direct
access, it remains inherently vulnerable to various attacks, including poisoning attacks …

Federated learning (FL) has achieved state-of-the-art performance in distributed learning
tasks with privacy requirements. However, it has been discovered that FL is vulnerable to …

Over the past years, the increasingly severe data island problem has spawned an emerging
distributed deep learning framework—federated learning, in which the global model can be …

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables
multiple clients to train a unified model without disclosing their private data. However …

Federated learning (FL) provides autonomy and privacy by design to participating peers,
who cooperatively build a machine learning (ML) model while keeping their private data in …

With the emergence of data silos and popular privacy awareness, the traditional centralized
approach of training artificial intelligence (AI) models is facing strong challenges. Federated …

Federated Learning (FL) is a distributed learning paradigm that enables different parties to
train a model together for high quality and strong privacy protection. In this scenario …