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 …

Local Differential Privacy (LDP) provides provable privacy protection for data collection
without the assumption of the trusted data server. In the real-world scenario, different data …

While significant progress has been made separately on analytics systems for scalable
stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics …

Security issues of artificial intelligence attract many attention in many research fields and
industries, such as face recognition, medical care, and client services. Federated learning is …

Federated learning is a recent advance in privacy protection. In this context, a trusted curator
aggregates parameters optimized in decentralized fashion by multiple clients. The resulting …

Federated learning is a recently proposed paradigm that presents significant advantages in
privacy-preserving machine learning services. It enables the deep learning applications on …

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 …

Federated learning (FL) is a training paradigm where the clients collaboratively learn
models by repeatedly sharing information without compromising much on the privacy of their …

Under the needs of processing huge amounts of data, providing high-quality service, and
protecting user privacy in artificial intelligence of things (AIoT), federated learning (FL) has …

Federated Learning (FL) is a paradigm for large-scale distributed learning which faces two
key challenges:(i) training efficiently from highly heterogeneous user data, and (ii) protecting …