The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

Federated learning (FL) aims to collaboratively train a shared model across multiple clients
without transmitting their local data. Data heterogeneity is a critical challenge in realistic FL …

The incidences of mental health illnesses, such as suicidal ideation and depression, are
increasing, which highlights the urgent need for early detection methods. There is a growing …

Federated learning, as a promising distributed learning paradigm, enables collaborative
training of a global model across multiple network edge clients without the need for central …

Federated Learning (FL) seeks to train a model collaboratively without sharing private
training data from individual clients. Despite its promise, FL encounters challenges such as …

Federated Learning (FL) enables collaborative model training among participants while
guaranteeing the privacy of raw data. Mainstream FL methodologies overlook the dynamic …

Multimodal Federated Learning (MFL) addresses the setup of multiple clients with diversified
modality types (eg image, text, video, and audio) working together to improve their local …