Big Data-Driven Federated Learning Model for Scalable and Privacy-Preserving Cyber Threat Detection in IoT-Enabled Healthcare Systems

The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats. The early detection of threats is both necessary and complex, yet these interconnected healthcare settings generate enormous amounts of heterogeneous data. Traditional Intrusion Detection Systems (IDS), which are generally centralized and machine learning-based, often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy. Moreover, traditional AI-driven IDS usually face challenges in handling large-scale, heterogeneous healthcare data while ensuring data privacy and operational efficiency. To address these issues, emerging technologies such as Big Data Analytics (BDA) and Federated Learning (FL) provide a hybrid framework for scalable, adaptive intrusion detection in IoT-driven healthcare systems. Big data techniques enable processing large-scale, high-dimensional healthcare data, and FL can be used to train a model in a decentralized manner without transferring raw data, thereby maintaining privacy between institutions. This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing, privacy, and compliance with ethical regulations. To strengthen the reliability of the reported findings, the results were validated using cross-dataset testing and 95% confidence intervals derived from bootstrap analysis, confirming consistent performance across heterogeneous healthcare data distributions. This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability, privacy, adaptability, and early-detection capabilities. The proposed global model achieves a test accuracy of 99.93% ± 0.03 (95% CI) and a miss-rate of only 0.07% ± 0.02, representing state-of-the-art performance in privacy-preserving intrusion detection. The proposed FL-driven IDS framework offers an efficient, privacy-preserving, and scalable solution for securing next-generation healthcare infrastructures by combining adaptability, early detection, and ethical data management.