12 citations · 26 across the 19 of their papers we have counts for
20 papers
Rethinking Structured Generation: Can Graph-Based Reasoning Resolve Ambiguity?
Ratun Rahman, Atit Pokharel
Structured generation is typically formulated as a deterministic mapping from input to a single structured output. This assumption is fundamentally misaligned with natural language…
Quantum Noise Mitigation with Adaptive Zero-Noise Extrapolation: A Contextual Multi-Armed Bandits Approach
Ratun Rahman, Dinh C. Nguyen
Variational quantum circuits (VQCs) are central to near-term quantum computing, yet their practical deployment is severely hindered by noise. While existing error mitigation method…
Scalable Malware Family Classification Using Quantum Kernel Based Machine Learning
Ratun Rahman, Hassan Jalil Hadi, Christopher Gabriel Pedraza Pohlenz +1
The classification of malware families is a key challenge in cybersecurity, which enables threat attribution, analysis of attack operations, and the formulation of effective defens…
Geometric Fairness-Aware Routing for Federated Edge Networks
Ratun Rahman
Emerging 6G and edge-intelligent networks require effective and balanced routing algorithms among varied and spatially distributed devices. Existing federated routing systems often…
Probabilistic Federated Learning on Uncertain and Heterogeneous Data with Model Personalization
Ratun Rahman, Dinh C. Nguyen
Conventional federated learning (FL) frameworks often suffer from training degradation due to data uncertainty and heterogeneity across local clients. Probabilistic approaches such…
Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach
Ratun Rahman, Shaba Shaon, Dinh C. Nguyen
Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quan…
When Quantum Federated Learning Meets Blockchain in 6G Networks
Dinh C. Nguyen, Md Bokhtiar Al Zami, Ratun Rahman +3
Quantum federated learning (QFL) is emerging as a key enabler for intelligent, secure, and privacy-preserving model training in next-generation 6G networks. By leveraging the compu…
Escaping Barren Plateaus in Variational Quantum Algorithms Using Negative Learning Rate in Quantum Internet of Things
Ratun Rahman, Dinh C. Nguyen
Variational Quantum Algorithms (VQAs) are becoming the primary computational primitive for next-generation quantum computers, particularly those embedded as resource-constrained ac…
Towards Heterogeneous Quantum Federated Learning: Challenges and Solutions
Ratun Rahman, Dinh C. Nguyen, Christo Kurisummoottil Thomas +1
Quantum federated learning (QFL) combines quantum computing and federated learning to enable decentralized model training while maintaining data privacy. QFL can improve computatio…
Towards Personalized Quantum Federated Learning for Anomaly Detection
Ratun Rahman, Sina Shaham, Dinh C. Nguyen
Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context…
Differentially Private Federated Quantum Learning via Quantum Noise
Atit Pokharel, Ratun Rahman, Shaba Shaon +2
Quantum federated learning (QFL) enables collaborative training of quantum machine learning (QML) models across distributed quantum devices without raw data exchange. However, QFL…
Quantum Federated Learning: A Comprehensive Survey
Dinh C. Nguyen, Md Raihan Uddin, Shaba Shaon +3
Quantum federated learning (QFL) is a combination of distributed quantum computing and federated machine learning, integrating the strengths of both to enable privacy-preserving de…
SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization
Ratun Rahman, Atit Pokharel, Md Raihan Uddin +1
Quantum federated learning (QFL) is an emerging field that has the potential to revolutionize computation by taking advantage of quantum physics concepts in a distributed machine l…
Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
Ratun Rahman, Atit Pokharel, Dinh C. Nguyen
Quantum Federated Learning (QFL) is an emerging paradigm that combines quantum computing and federated learning (FL) to enable decentralized model training while maintaining data p…
Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach
Atit Pokharel, Ratun Rahman, Thomas Morris +1
Quantum federated learning (QFL) has been recently introduced to enable a distributed privacy-preserving quantum machine learning (QML) model training across quantum processors (cl…
Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
Ratun Rahman
Federated Learning (FL) has emerged as a transformative paradigm in the field of distributed machine learning, enabling multiple clients such as mobile devices, edge nodes, or orga…
Brain Tumor Detection in MRI Based on Federated Learning with YOLOv11
Sheikh Moonwara Anjum Monisha, Ratun Rahman
One of the primary challenges in medical diagnostics is the accurate and efficient use of magnetic resonance imaging (MRI) for the detection of brain tumors. But the current machin…
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning
Ratun Rahman, Pablo Moriano, Samee U. Khan +1
Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) rec…
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach
Ratun Rahman, Neeraj Kumar, Dinh C. Nguyen
Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) are…
False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning
Md Raihan Uddin, Ratun Rahman, Dinh C. Nguyen
Smart metering networks are increasingly susceptible to cyber threats, where false data injection (FDI) appears as a critical attack. Data-driven-based machine learning (ML) method…