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两层累积量子化压缩,以实现通信效率高的联合学习:TLAQCC.

Yaoyao Ren1, Yu Cao2, Chengyin Ye1

  • 1School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, Liaoning, People's Republic of China.

Scientific reports
|July 19, 2023
PubMed
概括

联合学习通信成本通过TLAQC算法降低. 这种方法使用修订后的量子化与零值校正和自适应值来最大限度地减少错误和通信回合,提高模型准确性.

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科学领域:

  • 机器学习 机器学习
  • 分布式系统 分布式系统
  • 数据压缩数据压缩

背景情况:

  • 联合学习 (FL) 允许在不集中数据的情况下进行协作机器学习.
  • 由于频繁的梯度转移,高通讯成本是FL的一个主要瓶,特别是对于深度模型.
  • 现有的FL方法难以平衡通信效率和模型准确性.

研究的目的:

  • 提出一种新的算法,即双层累积量子化压缩 (TLAQC),以显著降低联合学习中的通信成本.
  • 通过最大限度地减少个人沟通开销和全球沟通轮次数,提高联合学习的效率.
  • 为了减轻通信压缩技术固有的精度损失.

主要方法:

  • 引入了修订的定量化静态梯度下降 (RQSGD) 与零值校正,以减少无效的定量化和最大限度地减少平均定量化错误.
  • 实施了自适应值和参数自检机制,以减少梯度信息上传的频率.
  • 采用了双层积累策略,通过积累量化错误和保留重量三角形来补偿梯度知识损失.

主要成果:

  • RQSGD实现了0.003%的无效量化发生率,并将平均量化误差降低到1.6 × 10−2.2.
  • TLAQC将上传的流量压缩到完全精确的FedAVG的6.73%.
  • 与完全精确的FedAVG相比,TLAQC将模型准确度提高了1.25%.

结论:

  • 通过先进的量化和积累技术,TLAQC有效地降低了联合学习中的通信成本.
  • 拟议的RQSGD方法显著降低了量子化错误和无效的量子化.
  • TLAQC为高效的联合学习提供了一个有前途的解决方案,提高了准确性,同时大大减少了通信开销.