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.

翻译：独特的艺术风格对艺术家的职业竞争力至关重要，但现有艺术委托平台极少支持基于风格的检索。同时，快速发展的生成式人工智能技术加剧了艺术家对在公开平台发布个人艺术作品的担忧。为实现不暴露个人艺术作品的风格检索，我们提出FedStyle——一种基于风格的联邦学习众包框架。该框架允许艺术家训练本地风格模型并共享模型参数（而非艺术作品）以进行协作。然而，多数艺术家拥有独特的艺术风格，导致参与者之间存在严重的模型漂移。FedStyle通过让艺术家学习抽象风格表征并与服务器对齐，而非简单聚合缺乏语义信息的模型参数，以应对这种极端的数据异质性。此外，我们引入对比学习精心构建风格表征空间，在嵌入空间中拉近风格相近的艺术作品，推离风格不同的艺术作品。在提出的数据集上进行的大量实验证明了FedStyle的优越性。