2 citations · 2 across the 10 of their papers we have counts for
10 papers
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…
Latency-aware Multimodal Federated Learning over UAV Networks
Shaba Shaon, Dinh C. Nguyen
This paper investigates federated multimodal learning (FML) assisted by unmanned aerial vehicles (UAVs) with a focus on minimizing system latency and providing convergence analysis…
Empowering AI-Native 6G Wireless Networks with Quantum Federated Learning
Shaba Shaon, Md Raihan Uddin, Dinh C. Nguyen +3
AI-native 6G networks are envisioned to tightly embed artificial intelligence (AI) into the wireless ecosystem, enabling real-time, personalized, and privacy-preserving intelligenc…
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…