Intrusion detection (ID) is critical in securing computer networks against various malicious
attacks. Recent advancements in machine learning (ML), deep learning (DL), federated …

Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …

The demand for intelligent industries and smart services based on big data is rising rapidly
with the increasing digitization and intelligence of the modern world. This survey …

Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

Decentralized learning (DL) enables collaborative learning without a server and without
training data leaving the users' devices. However, the models shared in DL can still be used …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

This paper proposes a new Signal-to-interferenceplus-noise ratio (SINR)-based Device
selection, Power control, and Reconfigurable intelligent surface (RIS) configuration (SDPR) …

Various data-sharing platforms have emerged with the growing public demand for open data
and legislation mandating certain data to remain open. Most of these platforms remain …

Blockchained Federated Learning (FL) has been gaining traction for ensuring the integrity
and traceability of FL processes. Blockchained FL involves participants training models …

Fast and reliable interpretation of high-dimensional hyperspectral images (HSIs) can
provide great support to remote sensing-based Earth observations. Targets of interest in HSI …