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arXiv · 2503.12389

FedGAI: Federated Style Learning with Cloud-Edge Collaboration for Generative AI in Fashion Design

Mingzhu Wu·Jianan Jiang·Xinglin Li·Hanhui Deng·Di Wu
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Abstract

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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Mingzhu Wu, Jianan Jiang, Xinglin Li, Hanhui Deng, Di Wu. 2025-03-16. FedGAI: Federated Style Learning with Cloud-Edge Collaboration for Generative AI in Fashion Design. https://arxiv.org/abs/2503.12389

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