Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …

Federated learning (FL) has been increasingly considered to preserve data training privacy
from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT) …

In fifth-generation (5G) networks and beyond, communication latency and network
bandwidth will be no longer be bottlenecks to mobile users. Thus, almost every mobile …

Deep learning methods have significantly progressed in hyperspectral image (HSI)
classification. However, deep learning relies on large labeled data for training. The cost of …

In recent years, hyperspectral image change detection (HSI-CD) based on deep learning
has achieved high detection accuracy, but these methods obtain excellent detection results …

Reinforcement learning has a demand for massive data in complex problems, which makes
it infeasible to be applied to real cases where sampling is difficult. The key to coping with …

In the federated learning paradigm, multiple mobile clients train their local models
independently based on the datasets generated by edge devices, and the server …

Active learning is focused on minimizing the effort required to obtain labeled data by
iteratively choosing fresh data samples for training a machine learning model. One of the …

We propose two novel stochastic gradient descent algorithms, ByGARS and ByGARS++, for
distributed machine learning in the presence of any number of Byzantine adversaries. In …

Federated learning has emerged as a promising distributed machine learning paradigm to
preserve data privacy. One of the fundamental challenges of federated learning is that data …