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 …

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 …

The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

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

In recent years, federated learning has rapidly become a new research hotspot in the field of
secure machine learning. However, unprotected traditional federated learning can easily …

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 …

As a popular machine learning method, federated learning (FL) can effectively solve the
issues of data silos and data privacy. However, traditional federated learning schemes …

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) requires frequent uploading and updating of model parameters,
which is naturally vulnerable to gradient leakage attacks (GLAs) that reconstruct private …

Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …