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) provides a variety of privacy advantages by allowing clients to
collaboratively train a model without sharing their private data. However, recent studies have …

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), a privacy-preserving training approach, has proven to be effective,
yet its vulnerability to attacks that extract information from model weights is widely …

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) has emerged as a promising privacy-preserving machine learning
paradigm, enabling data owners to collaboratively train a joint model by sharing model …

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 …

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 …

Robustness and privacy security are two important factors of trustworthy federated learning
(FL). Existing FL works usually secure data privacy by perturbing local model gradients via …

Federated Learning (FL) enables multiple distributed clients to collaboratively train a model
with owned datasets. To avoid the potential privacy threat in FL, researchers propose the DP …