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

Visual Internet-of-Things (IoT) cameras and institution-controlled edge gateways increasingly collect artwork images in museums, galleries, and heritage sites. Centralizing these images can expose collection contents, exhibition layouts, and contextual information. This paper proposes FedArtSense, a privacy-preserving federated learning framework for artistic style and medium classification. FedArtSense combines discrepancy-adaptive dual-space prototype alignment, client-level differential privacy for model and prototype releases, and importance-aware shared sparsification compatible with secure aggregation. Experiments on WikiArt, ArtBench-10, and a seven-class Behance Artistic Media subset use emulated non-IID client partitions, persistent acquisition shifts, constrained uplinks, and client dropout. Under the default client-level target (ϵ,δ)=(6,10-5), FedArtSense obtains accuracies of 64.2%, 81.2%, and 75.3%, respectively, while reducing cumulative WikiArt uplink traffic to 11.5 GiB. The results support FedArtSense as a privacy-utility-communication trade-off for gateway-assisted artistic image classification; retrieval, detection, aesthetic prediction, and direct battery-powered camera training are outside the evaluated scope.