Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …

In a federated learning scenario where multiple parties jointly learn a model from their
respective data, there exist two conflicting goals for the choice of appropriate algorithms. On …

Semantic communication has been identified as a core technology for the sixth generation
(6G) of wireless networks. Recently, task-oriented semantic communications have been …

The proliferation of edge computing and the Internet of Vehicles (IoV) has significantly
bolstered the popularity of deep learning-based driver assistance applications. This has …

Learning with graphs has attracted significant attention recently. Existing representation
learning methods on graphs have achieved state-of-the-art performance on various graph …

At present, there is a pressing need for data scientists and academic researchers to devise
advanced machine learning and artificial intelligence-driven systems that can effectively …

Social chatbots, also known as chit-chat chatbots, evolve rapidly with large pretrained
language models. Despite the huge progress, privacy concerns have arisen recently …

Federated Learning (FL) trains a black-box and high-dimensional model among different
clients by exchanging parameters instead of direct data sharing, which mitigates the privacy …

Abstract Automated Essay Scoring (AES) is a challenging topic in Natural Language
Processing. Recently, deep learning models have achieved remarkable performance for the …

Deep learning classifiers are crucial in the age of artificial intelligence. The device-edge-
based collaborative inference has been widely adopted as an efficient framework for …