The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved
great success and become a milestone in the field of artificial intelligence (AI). Owing to …

In the coming 6G communications, network densification, high throughput, positioning
accuracy, energy efficiency, and many other key performance indicator requirements are …

Meta-learning for few-shot learning entails acquiring a prior over previous tasks and
experiences, such that new tasks be learned from small amounts of data. However, a critical …

Transfer learning is an emerging topic that may drive the success of machine learning in
research and industry. The lack of data on specific tasks is one of the main reasons to use it …

Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for
quickly improving performance on a novel task. Bayesian hierarchical modeling provides a …

There is no doubt that big data are now rapidly expanding in all science and engineering
domains. While the potential of these massive data is undoubtedly significant, fully making …

Electroencephalogram (EEG) has been widely used in emotion recognition due to its high
temporal resolution and reliability. Since the individual differences of EEG are large, the …

Abstract Machine learning and data mining techniques have been used in numerous real-
world applications. An assumption of traditional machine learning methodologies is the …