FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art Commissions

TL;DR

FedStyle is a federated learning framework that enables artists to collaboratively develop style-based models without sharing personal artworks, effectively handling style heterogeneity and improving retrieval accuracy.

Contribution

It introduces a novel federated learning approach with style representation alignment and contrastive learning to address data heterogeneity in art style modeling.

Findings

FedStyle outperforms baseline methods in style retrieval accuracy.

The framework effectively handles extreme data heterogeneity among artists.

Contrastive learning enhances style representation quality.

Abstract

The unique artistic style is crucial to artists' occupational competitiveness, yet prevailing Art Commission Platforms rarely support style-based retrieval. Meanwhile, the fast-growing generative AI techniques aggravate artists' concerns about releasing personal artworks to public platforms. To achieve artistic style-based retrieval without exposing personal artworks, we propose FedStyle, a style-based federated learning crowdsourcing framework. It allows artists to train local style models and share model parameters rather than artworks for collaboration. However, most artists possess a unique artistic style, resulting in severe model drift among them. FedStyle addresses such extreme data heterogeneity by having artists learn their abstract style representations and align with the server, rather than merely aggregating model parameters lacking semantics. Besides, we introduce contrastive learning to meticulously construct the style representation space, pulling artworks with similar styles closer and keeping different ones apart in the embedding space. Extensive experiments on the proposed datasets demonstrate the superiority of FedStyle.