联邦学习让设计师协作生成多风格服装草图，不传数据也能互相借鉴。
FedGAI: Federated Style Learning with Cloud-Edge Collaboration for Generative AI in Fashion Design
- 用联邦学习实现云端与边缘协同，保护隐私地共享草图风格。
- 生成的草图质量接近人工设计，效率远超手绘。
- 适合需要创意协作但不愿公开设计数据的时尚设计师。
协作能融合多元创意与视觉元素，推动设计创新。在协同设计中，草图是表达设计灵感的关键载体，但设计师常不愿公开精心绘制的草图，形成设计领域的数据孤岛，阻碍了第三次AI浪潮下的数字化转型。本文提出一种联邦生成式人工智能服装系统FedGAI，采用联邦学习支持草图设计协作。FedGAI致力于构建一个设计师间可交换草图风格的生态体系。通过该系统，设计师可在不披露或上传数据的前提下，生成融合多位同行风格的草图，获取灵感。大量性能评估表明，FedGAI生成的多风格草图质量可媲美人工设计，且效率显著优于手工绘制。
原文摘要 · Abstract (English)
Collaboration can amalgamate diverse ideas, styles, and visual elements, fostering creativity and innovation among different designers. In collaborative design, sketches play a pivotal role as a means of expressing design creativity. However, designers often tend to not openly share these meticulously crafted sketches. This phenomenon of data island in the design area hinders its digital transformation under the third wave of AI. In this paper, we introduce a Federated Generative Artificial Intelligence Clothing system, namely FedGAI, employing federated learning to aid in sketch design. FedGAI is committed to establishing an ecosystem wherein designers can exchange sketch styles among themselves. Through FedGAI, designers can generate sketches that incorporate various designers' styles from their peers, drawing inspiration from collaboration without the need for data disclosure or upload. Extensive performance evaluations indicate that our FedGAI system can produce multi-styled sketches of comparable quality to human-designed ones while significantly enhancing efficiency compared to hand-drawn sketches.
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