Privacy-Preserving Federated Learning for Artistic Image Analytics in Visual IoT Sensor Networks

Visual Internet-of-Things (IoT) sensors are increasingly used to collect artistic images in museums, galleries, cultural heritage sites, and public spaces. Centralizing these images for analysis, however, can expose sensitive information concerning artwork ownership, exhibition layouts, visitor activities, and institutional collections. Federated learning offers a decentralized alternative, but its application is challenged by non-independent and identically distributed image data, resource-constrained sensor nodes, communication overhead, and privacy leakage from model updates. This paper proposes FedArtSense, a privacy-preserving federated learning framework for artistic image analytics in visual IoT sensor networks. FedArtSense introduces prototype-guided representation alignment to reduce client drift caused by heterogeneous artistic styles and collection distributions. An adaptive privacy mechanism dynamically determines gradient-clipping thresholds and noise levels according to update sensitivity, while a Rényi differential privacy accountant provides quantifiable privacy guarantees. In addition, importance-aware sparse aggregation reduces communication costs by transmitting only informative model updates. Experiments on the WikiArt, ArtBench-10, and Behance Artistic Media datasets under realistic non-IID and resource-constrained IoT settings demonstrate that FedArtSense consistently improves classification performance and convergence stability compared with representative federated learning and privacy-preserving baselines. It also achieves a favorable balance among analytical accuracy, privacy protection, and communication efficiency. These results indicate that FedArtSense provides an effective solution for secure and scalable artistic image analysis across distributed visual IoT infrastructures.