Privacy-Enhanced Federated Learning Model for Secure Internet of Things Applications: A Systematic Review
DOI:
https://doi.org/10.26740/vubeta.v3i3.46474Keywords:
Edge Intelligence, Cybersecurity, Privacy Enhancing, Internet of Things, Federated LearningAbstract
The rapid expansion of Internet of Things (IoT) ecosystems has intensified security and privacy concerns due to massive volume of sensitive, distributed, and heterogeneous data generated by connected devices. Conventional machine learning approaches for IoT security are increasingly becoming unsuitable because they require raw data aggregation, leading to privacy risks, scalability issues, and regulatory challenges. This study adopts a systematic literature review to examine recent advancements in privacy-enhanced federated learning (PEFL) for secure IoT. Relevant peer-reviewed journal articles and conference papers were identified, screened, and analyzed to investigate FL architectures, optimization techniques, privacy-preserving mechanisms, and cybersecurity applications within heterogeneous IoT environments. A structured taxonomy is developed to classify existing approaches, identify prevailing research trends. It reveals that federated learning significantly reduces data exposure risks by keeping sensitive IoT data localized at edge devices. Privacy-enhancing techniques such as differential privacy, secure aggregation, and cryptographic methods improve confidentiality but often introduce trade-offs in model accuracy and communication efficiency. Hierarchical and adaptive FL architectures demonstrate improved scalability and robustness in large-scale IoT networks. FL-based intrusion detection systems show strong potential for distributed anomaly detection while preserving data privacy. However, performance degradation due to non-IID data and system heterogeneity remains a persistent challenge. Resource constraints on edge devices limit the adoption of complex privacy-preserving techniques. Experimental evaluations are predominantly simulation-based, with limited real-world deployments. The absenceof standardized benchmarks further complicates cross-study comparisons. PEFL represents a promising foundation for secure IoT systems, but further research is required toaddress practical deployment challenges and enhance real-world applicability.
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