DFedGFM:通过全球平面性和全球势头,追求分散式联合学习的全球一致性

Qinglun Li1, Miao Zhang1, Tao Sun1

  • 1National University of Defense Technology, No. 109 Deya Road, Chang Sha, 410073, Hu Nan, China.

概括

像DFedGFM这样的分散式联合学习 (DFL) 算法通过使用全球信息来解决客户端漂移,以获得更好的一致性. 这种新的方法提高了对异质数据的稳定性,并实现了更紧密的融合率.

相关概念视频

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