As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …

Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

Federated learning has emerged as an effective paradigm to achieve privacy-preserving
collaborative learning among different parties. Compared to traditional centralized learning …

With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …

Recently, there has been a surge in the development of advanced intelligent generative
content (AIGC), especially large language models (LLMs). However, for many downstream …

Large Language Models (LLMs) are transforming the landscape of mobile intelligence.
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine …

Recently, edge AI has been launched to mine and discover valuable knowledge at network
edge. Federated Learning, as an emerging technique for edge AI, has been widely …

Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …

Multi-modal sensing systems are increasingly prevalent in real-world applications such as
health monitoring and autonomous driving. Most multi-modal learning approaches need to …

Federated Learning (FL) has been considered as an appealing framework to tackle data
privacy issues of mobile devices compared to conventional Machine Learning (ML). Using …