Federated Learning for Personalized Image Aesthetics Assessment

Image aesthetics assessment (IAA) evaluates the generic aesthetic quality of images. Due to the subjectivity of IAA, personalized IAA (PIAA) is essential to offering dedicated image retrieval, editing, and recommendation services to individual users. However, existing PIAA approaches are trained under the centralized machine learning paradigm, which exposes sensitive image and rating data. To enhance PIAA in a privacy-preserving manner, we propose the first-of-its-kind Federated Learning-empowered Personalized Image Aesthetics Assessment (FedPIAA) approach with a simple yet effective model structure to capture image aesthetic patterns and personalized user aesthetic preferences. Extensive experimental comparison against eight baselines using the real-world dataset FLICKER-AES demonstrates that FedPIAA outperforms FedAvg by 1.56% under the small support set and by 4.86% under the large support set in terms of Spearman rank-order correlation coefficient between predicted and ground-truth personalized aesthetics scores, while achieving comparable performance with the best non-FL centralized PIAA approaches.