In recent years, as the boundaries of computing have expanded with the emergence of the
Internet of Things (IoT) and its increasing number of devices continuously producing flows of …

With the development of the technologies deployed on vehicles, there is a significant
increase in the amount of data, which comes from various applications, such as battery …

Federated Learning (FL) has emerged as a powerful approach that enables collaborative
distributed model training without the need for data sharing. However, FL grapples with …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-
empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to …

With the quick evolution of giant models, the paradigm of pre-training models and then fine-
tuning them for downstream tasks has become increasingly popular. The adapter has been …

Data heterogeneity impacts the performance of Federated Learning (FL) by introducing
training noise. Although representative client sampling can help mitigate the issue, it …

Federated Learning (FL) is extensively used to train AI/ML models in distributed and privacy-
preserving settings. Participant edge devices in FL systems typically contain non …

Security and efficiency are two desirable properties of federated learning (FL). To enforce
data security for FL participants, homomorphic encryption (HE) is widely adopted. However …

Recent advances in remote sensing technologies have led to an explosive growth of geo-
spatiotemporal data in fields like geology, ecology, hydrology, and astronomy. To effectively …