Wireless communication at the terahertz (THz) frequency bands (0.1–10 THz) is viewed as
one of the cornerstones of tomorrow's 6G wireless systems. Owing to the large amount of …

Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices
without compromising their privacy. As computing tasks are increasingly performed by a …

AI Safety is an emerging area of critical importance to the safe adoption and deployment of
AI systems. With the rapid proliferation of AI and especially with the recent advancement of …

Emerging cross-device artificial intelligence (AI) applications require a transition from
conventional centralized learning systems toward large-scale distributed AI systems that can …

A recent take toward federated analytics (FA), which allows analytical insights of distributed
data sets, reuses the federated learning (FL) infrastructure to evaluate the summary of model …

Federated learning allows a group of distributed clients to train a common machine learning
model on private data. The exchange of model updates is managed either by a central entity …

In a practical setting, how to enable robust Federated Learning (FL) systems, both in terms of
generalization and personalization abilities, is one important research question. It is a …

Federated learning (FL) is an appealing paradigm for learning a global model among
distributed clients while preserving data privacy. Driven by the demand for high-quality user …

As probably the most complicated and critical infrastructure system, US power grids become
increasingly vulnerable to extreme events such as cyber-attacks and severe weather, as …

Nowadays, Federated Learning has emerged as the prominent collaborative learning
approach among multiple machine learning techniques. This framework enables …