Deep transfer learning (DTL), which incorporates new ideas from deep neural networks into
transfer learning (TL), has achieved excellent success in computer vision, text classification …

Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Federated Learning (FL) refers to the decentralized and privacy-preserving machine
learning framework in which multiple clients collaborate (with the help of a central server) to …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

Existing video-audio understanding models are trained and evaluated in an intra-domain
setting, facing performance degeneration in real-world applications where multiple domains …

Out-of-distribution generalization is challenging for non-participating clients of federated
learning under distribution shifts. A proven strategy is to explore those invariant relationships …

Medical image segmentation under federated learning (FL) is a promising direction by
allowing multiple clinical sites to collaboratively learn a global model without centralizing …

The maturation of sensor network technologies has promoted the emergence of the
Industrial Internet of Things, which has been collecting an increasing volume of monitoring …