Federated learning (FL) has attracted growing interest for enabling privacy-preserving
machine learning on data stored at multiple users while avoiding moving the data off-device …

Federated Learning (FL) aims to improve machine learning privacy by allowing several data
owners in edge and ubiquitous computing systems to collaboratively train a model, while …

Federated learning (FL) protects training data in clients by collaboratively training local
machine learning models of clients for a global model, instead of directly feeding the training …

Secure aggregation is a critical component in federated learning (FL), which enables the
server to learn the aggregate model of the users without observing their local models …

Federated learning (FL) aims to perform privacy-preserving machine learning on distributed
data held by multiple data owners. To this end, FL requires the data owners to perform …

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) is a popular distributed learning framework that can reduce privacy
risks by not explicitly sharing private data. However, recent works demonstrated that sharing …

Federated learning (FL) aims to collaboratively train the global model in a distributed
manner by sharing the model parameters from local clients to a central server, thereby …

Federated Learning is an emerging privacy-preserving distributed machine learning
approach to building a shared model by performing distributed training locally on …