Academic certificate fraud is a persistent problem across developing economies, undermining trust between graduates, employers, and higher education institutions. In Nigeria, this has prompted a federal mandate requiring every employer to verify staff credentials; yet existing verification processes remain manual, slow, and easily circumvented. This paper proposed, implemented, and evaluated a fully decentralized academic certificate management framework built on the Ethereum blockchain, leveraging Solidity smart contracts, the InterPlanetary File System (IPFS) for off-chain document storage, QR-code-enabled instant verification, and on-chain revocation.The system was deployed and tested on the Ethereum Sepolia testnet using Hardhat 2.19.4 and ethers.js 6.x. Results show that issuing a single certificate costs a fraction of a cent, that batch issuance lowers this cost further, that verification is free for employers, and that the system correctly detects fabricated or altered certificates in the overwhelming majority of cases while cutting verification time from days to seconds. These findings demonstrate that blockchain-based credential management is technically sound, affordable, and readily deployable in resource-constrained institutional settings, offering a practical path toward restoring trust in academic credentials across Nigerian and other African universities.
Miriam W. Kaara, Jael S. Wekesa, Michael W. Kimwele
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.