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 …

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) enables distributed agents to collaboratively learn a centralized
model without sharing their raw data with each other. However, data locality does not …

Providing privacy protection has been one of the primary motivations of Federated Learning
(FL). Recently, there has been a line of work on incorporating the formal privacy notion of …

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) …

Conventional federated learning directly averages model weights, which is only possible for
collaboration between models with homogeneous architectures. Sharing prediction instead …

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 …

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 …

User-level differential privacy (DP) provides certifiable privacy guarantees to the information
that is specific to any user's data in federated learning. Existing methods that ensure user …

We consider the problem of reinforcing federated learning with formal privacy guarantees.
We propose to employ Bayesian differential privacy, a relaxation of differential privacy for …