12 citations · 26 across the 19 of their papers we have counts for
12 papers · 1 filter
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…