Loading Now

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)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

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