QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
By Nouhaila Innan, Alberto Marchisio, Muhammad Shafique, Mohamed Bennai
Rating
1043
Battle Count: 76
Relevance
7/10
While focused on fraud detection, the framework's ability to handle sensitive financial data securely could be adapted for quantitative trading applications.
Implementation Complexity
8/10
Requires expertise in both quantum computing and federated learning, as well as access to quantum computing resources.
Reproducibility
3/5
The paper provides details on the experimental setup and dataset used, but full code implementation is not provided.
About this paper
Methodology: Quantum Federated Neural Network. Problem types: Classification, Anomaly Detection.
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