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Quantum Federated Learning Experiments in the Cloud with Data Encoding
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Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy. We explore the challenges of deploying QFL on cloud platforms, emphasizing quantum intricacies and platform limitations. The proposed data-encoding-driven QFL, with a proof of concept (GitHub Open Source) using genomic data sets on quantum simulators, shows promising results.
Forward citations
Cited by 3 Pith papers
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A Drift Stable Quantum Federated Learning for Intelligent Services
DUQFL-Prox combines deep-unfolded SPSA optimization, proximal drift control, and a validation-guided controller to stabilize quantum federated learning under heterogeneous clients.
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New Insights on Unfolding and Fine-tuning Quantum Federated Learning
Deep unfolding with client-learned hyperparameters is claimed to improve quantum federated learning accuracy from roughly 55% to 90%, but the supporting proof and baseline data are not established.
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LLM-QFL: Distilling Large Language Model for Quantum Federated Learning
LLM-QFL uses locally fine-tuned LLMs as controllers to reduce communication rounds and adapt optimizer steps in quantum federated learning.
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