Enhancing Privacy-Preserving Data Mining in Cloud-Based Data Warehouses: A Federated Learning Approach for Secure Multi-Tenant Environments

Many organisations utilise cloud-based data warehouses for their storage and analytics because these systems support multiple user tenants yet present considerable privacy threats. Traditional centralised data mining devices are sensitive to security risks, thus making federated learning (FL) an attractive solution for protecting information during mining processes. A research investigation analyses FedAvg and FedProx through a federated system while adding DP and secure aggregation technology to address security requirements. Tests demonstrate that SF-XGBoost delivers 99.31% accuracy alongside 0.9994 ROC-AUC values, which exceeds the other studied models in both centralised and federated learning platforms. The stability performance of FedProx is higher than that of FedAvg, although its precision and recall capabilities fall behind. Patronage clients that applied differentially private SF-XGBoost recorded 73.75% data accuracy while displaying privacy-utility configuration aspects. The slightly more significant losses of federated models parallel their ability to protect data privacy while satisfying privacy regulations. SF-XGBoost and privacy-improving methods deliver an effective security solution for cloud-based federated learning applications. Scientists must investigate how homomorphic encryption works alongside adaptive privacy budgets and federated transfer learning to achieve maximum optimisation of federated models.