5 papers
A Stable Aggregation Method for Quantum Federated Learning
Shanika Nanayakkara, Shiva Raj Pokhrel
Quantum federated learning (QFL) enables clients to train quantum neural network (QNN) models without sharing private data. We find that aggregation in QFL is unstable under hetero…
A Drift Stable Quantum Federated Learning for Intelligent Services
Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel
Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services. Inte…
HantaWatch: Federated Learning for Hantavirus Genomic Surveillance
Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel
Hantavirus genomic surveillance is limited by the distribution of sequence data, non-IID source heterogeneity, and constrained expert-review capacity. We propose HantaWatch, a fede…
Adaptive Aggregation with Two Gains in QFL
S Nanayakkara
Federated learning (FL) deployed over quantum enabled and heterogeneous classical networks faces significant performance degradation due to uneven client quality, stochastic telepo…
New Insights on Unfolding and Fine-tuning Quantum Federated Learning
Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel
Client heterogeneity poses significant challenges to the performance of Quantum Federated Learning (QFL). To overcome these limitations, we propose a new approach leveraging deep u…