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 …

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

Federated Learning (FL) aims to train high-quality models in collaboration with distributed
clients while not uploading their local data, which attracts increasing attention in both …

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 …

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

Natural language processing (NLP) sees rich mobile applications. To support various
language understanding tasks, a foundation NLP model is often fine-tuned in a federated …

Along with the vast application of Internet of Things (IoT) and the ever-growing concerns
about data protection, a novel type of learning, named incremental federated learning (IFL) …

Energy efficiency is essential for federated learning (FL) over mobile devices and its
potential prosperous applications. Different from existing communication efficient FL …

Machine learning models have been deployed in mobile networks to deal with massive data
from different layers to enable automated network management and intelligence on devices …