Background Artificial intelligence (AI) has revolutionized health care delivery in recent years.
There is an increase in research for advanced AI techniques, such as deep learning, to build …

Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

To defend the inference attacks and mitigate the sensitive information leakages in Federated
Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy …

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

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 …

Privacy and communication constraints are two major bottlenecks in federated learning (FL)
and analytics (FA). We study the optimal accuracy of mean and frequency estimation …

Although federated learning can provide privacy protection for individual raw data, some
studies have shown that the shared parameters or gradients under federated learning may …

Communication is a key bottleneck in federated learning where a large number of edge
devices collaboratively learn a model under the orchestration of a central server without …