qinnovision-world-challenge-2025 / Quantum Federated Learning for Fraud Detection

This use case focuses on Quantum Federated Learning (QFL) for fraud detection in financial systems, highlighting the importance of maintaining privacy while identifying fraudulent activities. Financial institutions often face challenges when handling sensitive data and ensuring privacy during model training. QFL decentralizes the training process, leveraging quantum techniques to enhance efficiency and safeguard privacy.

The goal of this challenge is to design a QFL architecture specifically for fraud detection, tackling key technical obstacles such as data privacy, scalability, and accuracy. Participants must propose solutions that demonstrate practical implementation and address the unique complexities of quantum and federated learning integration.

This competition is part of the QInnovision World Challenge 2025 hosted on Aqora. Join the event. (https://aqora.io/events/qinnovision-world-challenge-2025)