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 …

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 …

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

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 …

Personalized federated learning (PFL), as a novel federated learning (FL) paradigm, is
capable of generating personalized models for heterogenous clients. Combined with a meta …

While preserving the privacy of federated learning (FL), differential privacy (DP) inevitably
degrades the utility (ie, accuracy) of FL due to model perturbations caused by DP noise …

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 …

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 is a promising distributed machine learning paradigm that has been
playing a significant role in providing privacy-preserving learning solutions. However …