RPIFL: Reliable and Privacy-Preserving Federated Learning for the Internet of Things

Abstract

Federated Learning for the Internet of Things(FL for IoT) contributes to the enhancement of security in smart cities. However, the privacy disclosure attacks within the FL for IoT framework exposes user’s local data . Although differential privacy mechanism (DP) ensures the privacy of IoT data, one crucial issue is DP affects model accuracy and reduces model reliability. Besides, DP cannot provide comprehensive privacy preservation in the IoT environment. To tackle the above problems, we propose reliable federated learning with privacy-preserving for IoT(RPIFL). Our approach comprises three key components. First, we leverage a lightweight network to hide the private data locally, then use the output to perform the next model training. Second, we propose a multi-modal fine-grained model optimization, which significantly improves the accuracy of the IoT model. Third, we implement adaptive differential privacy mechanisms tailored to dynamic privacy requirements, thereby safeguarding the confidentiality of IoT data. To empirically validate our framework, we conducted a simulation experiment on an image classification dataset. Our methodology concurrently ensures privacy protection and achieves superior accuracy compared to the baseline model with an average accuracy increase of 1.1% over the two prevailing federated privacy protection methods, coupled with an average reduction in convergence time of over 9%.