Federated Learning Approaches for Privacy-Preserving Threat Detection in Smart Home IoT Environments

Abstract

Smart home Internet of Things (IoT) environments have become increasingly pervasive, offering convenience and automation while simultaneously introducing new cybersecurity vulnerabilities. Traditional centralized machine learning approaches for threat detection rely on aggregating sensitive user data into cloud servers, raising significant concerns regarding privacy, data security, and regulatory compliance. Federated learning (FL) has emerged as a promising paradigm that enables collaborative model training across distributed IoT devices without sharing raw data, thus preserving privacy while maintaining effective threat detection. This review paper explores the application of FL in privacy-preserving threat detection within smart home IoT systems, analyzing its strengths, limitations, and future potential. The discussion highlights how FL mitigates risks such as data leakage, adversarial attacks, and model inversion while ensuring scalability in heterogeneous device ecosystems. Moreover, the review examines existing frameworks, comparative case studies, and integration with complementary technologies like blockchain and differential privacy to enhance robustness. Challenges such as communication overhead, resource constraints, and model poisoning attacks are also critically addressed. By synthesizing recent advancements and identifying open research gaps, this paper provides a roadmap for leveraging FL in developing secure, scalable, and privacy-preserving threat detection systems for smart homes.