Advanced adversarial attacks such as membership inference and model memorization can
make federated learning (FL) vulnerable and potentially leak sensitive private data. Local …

Federated learning (FL) is an emerging privacy-preserving paradigm, where a global model
is trained at a central server while keeping client data local. However, FL can still indirectly …

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 …

As a means of decentralized machine learning, federated learning (FL) has recently drawn
considerable attentions. One of the prominent advantages of FL is its capability of preventing …

Federated Learning (FL) is a decentralized machine learning (ML) approach that keeps data
localized and often incorporates Differential Privacy (DP) to enhance privacy guarantees …

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 …

Federated learning (FL) enables multiple clients to collaboratively train a model with the
coordination of a central server. Although FL improves data privacy via keeping each client's …

Federated learning (FL) that enables edge devices to collaboratively learn a shared model
while keeping their training data locally has received great attention recently and can protect …

Federated Learning (FL) allows multiple participating clients to train machine learning
models collaboratively by keeping their datasets local and only exchanging model updates …

Local differential privacy (LDP) is a prominent approach and widely adopted in federated
learning (FL) to preserve the privacy of local training data. It also nicely provides a rigorous …