Nahideh Derakhshanfard

A resource-aware framework for energy and bandwidth efficient IoT intrusion detection using lightweight federated learning over SDN

The rapid growth of Internet of Things (IoT) deployments has widened the network attack surface while increasing the cost of centralised intrusion detection, particularly when raw traffic must be moved to remote servers. We propose HFL-SDN-IDS, a hierarchical, resource-aware framework that combines a lightweight federa...

Digital-Twin-Coordinated Predictive Resource Management in 6G Cellular-Edge Networks Using Spatio-Temporal Graph Learning and Federated Reinforcement Learning

An Intelligent Deep Reinforcement Learning Framework for Cost-Efficient and Energy-Aware Cloud Resource Management

The rapid growth of cloud computing and Internet of Things (IoT) applications has intensified the need for efficient resource allocation, energy management, and cost optimization in large-scale data centers. Traditional optimization and machine learning approaches, while effective in static environments, often fail to...

Human-centered design based on federated learning to optimize user experience on blockchain platforms

In modern decentralized environments, designing systems that are not only efficient and secure but also understandable and trustworthy for end-users has emerged as the central challenge. Blockchain and Federated Learning (FL) have become two seminal technologies that facilitate privacy-preserving and transparent proces...