haimengzhao/quantum-fed-infer
A quantum machine learning algorithm for quantum non-IID federated learning
This project offers a quantum machine learning algorithm designed for federated learning, where data from multiple sources is used for training without sharing the raw information, ensuring privacy. It takes in decentralized, non-identically distributed datasets from various clients and outputs an improved global quantum model, outperforming traditional federated learning methods in these challenging scenarios. Researchers and practitioners in quantum machine learning and privacy-preserving AI would use this.
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Use this if you are developing or researching quantum federated learning systems and need to maintain high model performance even when client data is diverse and not uniformly distributed, with a focus on communication efficiency.
Not ideal if your federated learning problem does not involve quantum algorithms or if your client data is known to be identically and independently distributed.
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29
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5
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 29, 2023
Commits (30d)
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