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通信效率高的非凸式联合学习与对上链和下链的错误反
IEEE transactions on neural networks and learning systems
|November 23, 2023
概括
我们推出了两种新的联合学习算法,EF21和LAG,以降低大规模在线学习中的通信成本. 这些方法大大减少了数据传输,而不会影响学习的准确性.
科学领域:
- 机器学习 机器学习
- 分布式系统 分布式系统
- 优化优化 优化优化
背景情况:
- 大规模的在线学习通常涉及非凸分布式优化,这在计算上具有挑战性.
- 联合学习系统面临通信瓶,特别是边缘设备上传带宽有限.
研究的目的:
- 开发具有沟通效率的非形联合学习算法.
- 为了应对上链和下链通信不对称性在联合学习中的挑战.
主要方法:
- 提出了两个新的算法:错误反2021 (EF21) 和Lazily聚合梯度 (LAG).
- 开发了EF21与LAG (EF-LAG) 合作,以降低上行通信成本.
- 引入双向EF-LAG (BiEF-LAG) 以减少上链和下链成本.
主要成果:
- EF21表现出比香草更好的性能 错误反.
- EF-LAG和BiEF-LAG显著降低了通信开支.
- 拟议的算法实现了与梯度下降 (GD) 相比的合率,用于光滑的非凸函数.
结论:
- 开发的算法有效地降低了非形联合学习中的通信成本.
- 这些方法保持学习质量,同时提高沟通效率.
- 经验结果证实了拟议的算法在合成和深度学习基准上的优越性.
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