A Robust Federated Learning Approach for Combating Attacks Against IoT Systems Under non-IID Challenges

2025-11-20

AI Summary

To address performance degradation of federated learning (FL) in Internet-of-Things (IoT) environments caused by non-independent and non-identically distributed (non-IID) data, this work systematically evaluates the robustness and convergence of three mainstream FL algorithms—FedAvg, FedProx, and Scaffold—in large-scale IoT intrusion detection. Leveraging the CICIoT2023 dataset, we construct multiple non-IID partitioning scenarios and quantitatively compare classification accuracy, communication efficiency, and model stability under resource-constrained and privacy-sensitive conditions. Our study is the first to empirically reveal distinct adaptation mechanisms to statistical heterogeneity: FedProx achieves superior accuracy under highly skewed data distributions, while Scaffold attains faster convergence in low-round communication settings. These findings fill a critical gap in empirical FL algorithm comparison for heterogeneous IoT deployments and establish a reproducible benchmark and optimization roadmap for lightweight, edge-secure federated detection models.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Large-scale security measurements

Abstract

In the context of the growing proliferation of user devices and the concurrent surge in data volumes, the complexities arising from the substantial increase in data have posed formidable challenges to conventional machine learning model training. Particularly, this is evident within resource-constrained and security-sensitive environments such as those encountered in networks associated with the Internet of Things (IoT). Federated Learning has emerged as a promising remedy to these challenges by decentralizing model training to edge devices or parties, effectively addressing privacy concerns and resource limitations. Nevertheless, the presence of statistical heterogeneity in non-Independently and Identically Distributed (non-IID) data across different parties poses a significant hurdle to the effectiveness of FL. Many FL approaches have been proposed to enhance learning effectiveness under statistical heterogeneity. However, prior studies have uncovered a gap in the existing research landscape, particularly in the absence of a comprehensive comparison between federated methods addressing statistical heterogeneity in detecting IoT attacks. In this research endeavor, we delve into the exploration of FL algorithms, specifically FedAvg, FedProx, and Scaffold, under different data distributions. Our focus is on achieving a comprehensive understanding of and addressing the challenges posed by statistical heterogeneity. In this study, We classify large-scale IoT attacks by utilizing the CICIoT2023 dataset. Through meticulous analysis and experimentation, our objective is to illuminate the performance nuances of these FL methods, providing valuable insights for researchers and practitioners in the domain.

Problem

• Developing robust federated learning for IoT attack detection
• Addressing statistical heterogeneity in non-IID data distributions
• Comparing FedAvg, FedProx and Scaffold algorithms' performance

Innovation

• Federated learning combats IoT attacks under non-IID
• Compares FedAvg FedProx Scaffold algorithms performance
• Uses CICIoT2023 dataset for large-scale attack classification