A Privacy-Preserving Federated Learning Framework for Collaborative Academic Certificate Fraud Detection Across Institutions
DOI:
https://doi.org/10.53819/81018102t2599Abstract
The integrity and credibility of educational institutions worldwide are being undermined by the growing issue of academic certificate fraud. The public's trust in the educational system, the integrity of job opportunities, and the legitimacy of authentic credentials are all negatively impacted by academic dishonesty. The majority of academic credentialing systems, both centralized and blockchain-based, focus on academic credential validation and immutability, even though academic institutions have embraced technology to improve academic credentialing procedures. This suggests that their support for intelligent and private fraud detection is minimal. In this work, we suggest an integrated method that combines smart anomaly detection with Federated Learning (FL). This makes it possible for several organizations to build a model for detecting certificate fraud without disclosing private information to other organizations. Each institution's private information is retained and incorporated into a global model. This enables the framework to handle concerns about data ownership and privacy as well as regulatory compliance. The framework uses Extreme Gradient Boosting (XGBoost) to identify anomalies in metadata and Convolutional Neural Networks (CNNs) to detect visual forgeries in certificates. A decentralized node is used to train the models, which are merged using the Federated Averaging (FedAvg) algorithm. The federated model performs well in anomaly detection, according to the experiments conducted. When compared to conventional centralized approaches, it significantly reduces false positives and false negatives, with an accuracy of up to 94% and an AUC of 0.97. The model's findings imply that a federated learning approach would make it possible for institutions to detect fraud in a secure, scalable, and cooperative manner. In this regard, this framework offers a workable way to create digital credential systems that are more trustworthy and protect privacy.
Key words: Federated Learning, Blockchain, certificate fraud detection, privacy preservation, distributed datasets, secure collaboration.
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