RPPFL: Random Projection-Based Personalized Federated Learning for IoT Intrusion Detection

Abstract

The rapid growth of the Internet of Things (IoT) in critical domains such as healthcare, smart cities, cybersecurity, and finance has led to the generation of large volumes of distributed data and increased susceptibility to cyberattacks. Although federated learning (FL)-based intrusion detection systems (IDS) have been introduced to support distributed learning and improve privacy, they still face several challenges, including performance limitations, high computation and communication overhead, and potential privacy attacks. To address these challenges, we propose a Random Projection-Based Personalized Federated Learning (RPPFL) framework for IoT intrusion detection. In the proposed framework, random projection, a lightweight one-way dimensionality reduction transformation, is applied at the IoT device level, which enhances privacy while lowering computational and communication costs. Furthermore, personalized federated learning at the fog layer reduces the computational burden on resource-constrained IoT devices and improves model robustness in environments where IoT data are non-independent and identically distributed (non-IID). We also introduce a conditional generative adversarial network (cGAN)-based privacy attack to evaluate the resilience of the proposed framework and demonstrate the effectiveness of our approach in preserving the privacy of IoT data. Experimental results on the RT-IoT 2022 and CIC-IoT 2023 datasets demonstrate that RPPFL provides high detection accuracy (above 95.0%) while preserving data privacy and reducing computation and communication overhead in dynamic IoT environments. The proposed framework is generalizable and applicable to a wide range of IoT intrusion detection scenarios.