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Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach
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quantum machine learningfederated learningprivacy-preservinghybrid models
2609.25082
Builder Relevance
1h ago80%
Abstract
This paper explores the integration of federated learning with hybrid quantum-classical models to enhance privacy and performance in multi-party settings.
Reality Card
Core Claim
Federated learning enables high-performing, privacy-preserving quantum-classical collaboration without centralizing raw data, achieving an accuracy increase from 0.7227 to 0.8757.
Method / Result
SBVFL raises accuracy from 0.7227 to 0.8757 compared to local training.
Limitations
The paper does not specify the authors or provide detailed methodology for replication.
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