Lightweight and privacy-preserving hierarchical federated learning mechanism for artificial intelligence-generated image content

Abstract

With the rapid development of artificial intelligence and Big Data, the application of artificial intelligence-generated image content (AIGIC) is becoming increasingly widespread in various fields. However, the image data utilized by AIGIC is diverse and often contains sensitive personal information, characterized by heterogeneity and privacy concerns. This leads to prolonged implementation times for image data privacy protection, and a high risk of unauthorized third-party access, resulting in serious privacy breaches and security risks. To address this issue, this paper combines Hierarchical Federated Learning (HFL) with Homomorphic Encryption to first address the encryption and transmission challenges in the image processing pipeline of AIGIC. Building upon this foundation, a novel HFL group collaborative training strategy is designed to further streamline the privacy protection process of AIGIC image data, effectively masking the heterogeneity of raw image data and achieving balanced allocation of computational resources. Additionally, a model compression algorithm based on pruning is introduced to alleviate the data transmission pressure in the image encryption process. Optimization of the homomorphic encryption modulo operations significantly reduces the computational burden, enabling real-time enhancement of image data privacy protection from multiple dimensions including computational and transmission resources. To verify the effectiveness of the proposed mechanism, extensive simulation verification of the lightweight privacy protection process for AIGIC image data was performed, and a comparative analysis of the time complexity of the mechanism was conducted. Experimental results indicate substantial advantages of the proposed algorithm over traditional real-time privacy protection algorithms in AIGIC.