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Updated: May 21, 2025

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顶部k 混杂的差异性隐私联合学习,用于异质数据

Di Xiao1, Xinchun Fan1, Lvjun Chen1

  • 1College of Computer Science, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
概括

联合学习 (FL) 有效地解决了数据异质性和隐私风险,使用了新的TopkSDP-FL框架. 这种方法提高了模型的准确性,并在分散的AI培训中显著降低了通信成本.

关键词:
通信成本 通信成本数据异质性数据异质性联合学习的联合学习.混合模型的混合模型.

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