As massive data are produced from small gadgets, federated learning on mobile devices
has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) …

Train machine learning models on sensitive user data has raised increasing privacy
concerns in many areas. Federated learning is a popular approach for privacy protection …

Federated learning (FL) allows to train a massive amount of data privately due to its
decentralized structure. Stochastic gradient descent (SGD) is commonly used for FL due to …

Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine
Learning (ML), where the ML model is trained in a decentralized manner by the clients …

Since traditional federated learning (FL) algorithms cannot provide sufficient privacy
guarantees, an increasing number of approaches apply local differential privacy (LDP) …

Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …

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) is a promising machine learning paradigm that enables the
analyzer to train a model without collecting users' raw data. To ensure users' privacy …

Local differential privacy (LDP) is a recently proposed privacy standard for collecting and
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …

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