As artificial intelligence (AI) transitions from research to deployment, creating the appropriate
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …

Federated learning (FL) becomes popular and has shown great potentials in training large-
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …

Existing foundation models are trained on copyrighted material. Deploying these models
can pose both legal and ethical risks when data creators fail to receive appropriate …

When trying to gain better visibility into a machine learning model in order to understand and
mitigate the associated risks, a potentially valuable source of evidence is: which training …

Over the last few years, the Shapley value, a solution concept from cooperative game theory,
has found numerous applications in machine learning. In this paper, we first discuss …

The goal of data attribution is to trace model predictions back to training data. Despite a long
line of work towards this goal, existing approaches to data attribution tend to force users to …

Estimating the difficulty of a dataset typically involves comparing state-of-the-art models to
humans; the bigger the performance gap, the harder the dataset is said to be. However, this …

Sixth generation (6G) enabled edge intelligence opens up a new era of Internet of
everything and makes it possible to interconnect people-devices-cloud anytime, anywhere …

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the
confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages …

Federated learning is promising in enabling large-scale machine learning by massive
clients without exposing their raw data. It can not only enable the clients to preserve the …