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 …

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 …

Auditability and verifiability are critical elements in establishing trustworthiness in federated
learning (FL). These principles promote transparency, accountability, and independent …

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 …

In this paper, we study how to optimize existing Non-Fungible Token (NFT) incentives. Upon
exploring a large number of NFT-related standards and real-world projects, we come across …

As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation,
ensuring rightful ownership and ethical use becomes paramount. AI service providers are …

Federated Learning (FL) is gaining widespread interest for its ability to share knowledge
while preserving privacy and reducing communication costs. Unlike Centralized FL …