The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

There has been an increasing trend of malware release, which raises the alarm for security
professionals worldwide. It is often challenging to stay on top of different types of malware …

Abstract In Internet of Things (IoT) applications, federated learning is commonly used for
distributedly training models in a privacy-preserving manner. Recently, federated learning is …

We introduce dataset multiplicity, a way to study how inaccuracies, uncertainty, and social
bias in training datasets impact test-time predictions. The dataset multiplicity framework asks …

Artificial Intelligence (AI) systems are changing workplaces. AI systems have the potential to
improve workplaces, but ensuring trustworthy use of AI in the workplace means addressing …

Federated learning, leveraging distributed data from multiple nodes to train a common
model, allows for the use of more data to improve the model while also protecting the privacy …

Data poisoning attack is a well-known attack against machine learning models, where
malicious attackers contaminate the training data to manipulate critical models and …

With the increasing penetration of renewables, the power system is facing unprecedented
challenges of low-inertia levels. The inherent ability of the system to defense disturbance …

The deep integration of edge computing and Artificial Intelligence (AI) in IoT (Internet of
Things)-enabled smart cities has given rise to new edge AI paradigms that are more …

Federated Learning (FL) offers a promising solution to the privacy concerns associated with
centralized Machine Learning (ML) by enabling decentralized, collaborative learning …