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 …

Federated learning (FL) was originally regarded as a framework for collaborative learning
among clients with data privacy protection through a coordinating server. In this paper, we …

Federated Learning (FL) has attracted extraordinary attention from the industry and
academia due to its advantages in privacy protection and collaboratively training on isolated …

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

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 …

Federated learning (FL) protects training data in clients by collaboratively training local
machine learning models of clients for a global model, instead of directly feeding the training …

Although federated learning improves privacy of training data by exchanging local gradients
or parameters rather than raw data, the adversary still can leverage local gradients and …

Federated Learning was deemed as a private distributed learning framework due to the
separation of data from the central server. However, recent works have shown that privacy …

Federated learning (FL) is a distributed machine learning paradigm that enables scattered
clients to collaboratively train a shared global model. FL is suitable for privacy-preserving …

Federated learning (FL) is a popular approach to facilitate privacy-aware machine learning
since it allows multiple clients to collaboratively train a global model without granting others …