10 papers
Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Design
Atit Pokharel, Shaba Shaon, Thomas Morris +1
Distributed quantum learning (DQL) has emerged as a promising paradigm to scale quantum-enhanced machine learning by interconnecting multiple quantum devices. However, for efficien…
Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networks
Shaba Shaon, Christopher G. Brinton, Dinh C. Nguyen
Quantum federated learning (QFL) combines the robust data processing of quantum computing with the privacy-preserving features of federated learning (FL). However, in large-scale w…
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…