Summary of Fedstyle: Style-based Federated Learning Crowdsourcing Framework For Art Commissions, by Changjuan Ran et al.
FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art Commissions
by Changjuan Ran, Yeting Guo, Fang Liu, Shenglan Cui, Yunfan Ye
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a federated learning crowdsourcing framework called FedStyle, which enables artists to train local style models without sharing their personal artworks. The framework addresses extreme data heterogeneity by having artists learn abstract style representations and align with the server. The approach also incorporates contrastive learning to construct a style representation space that pulls similar styles together and keeps different ones apart. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedStyle is designed to support artistic style-based retrieval on Art Commission Platforms, addressing concerns about releasing personal artworks in public platforms. Artists can train local style models and share model parameters rather than artworks, ensuring their creative work remains private. The framework handles extreme data heterogeneity by having artists learn abstract style representations and align with the server. |
Keywords
» Artificial intelligence » Federated learning
