通过压缩动量波器进行强健和通信高效的分布式学习.
IEEE transactions on neural networks and learning systems
|February 3, 2026
概括
这项研究引入了一种新的分布式学习方法,这种方法对拜占庭式失败有很强的抵抗力,而且在沟通方面也很有效. 它通过使用Polyak Momentum来减少噪音,更快地汇聚到更准确的解决方案.
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:
- 分布式学习在私有数据中训练大模型,但面临着拜占庭式的稳定性和通信效率挑战.
- 目前的方法需要完全的梯度,并汇聚到大型解决方案社区.
- 现有的拜占庭强大的方法在通信效率和梯度压缩方面扎.
研究的目的:
- 开发一种新的,强大的,沟通效率高的随机分布式学习方法.
- 为了达到一个更小的社区的融合,与理论的下限保持一致.
- 解决依赖完全梯度信息的现有方法的局限性.
主要方法:
- 利用Polyak Momentum减轻来自偏向压缩机和随机梯度的噪音.
- 开发一个随机分布式学习算法,没有批量大小要求.
- 为非凸的光滑损失函数提供具有严格复杂度限制的理论分析.
主要成果:
- 拟议的方法在信息压缩下证明了拜占庭的稳定性.
- 与现有方法相比,实现了与较小的社区的融合.
- 对二进制和图像分类任务的实验验证证证实了实际意义.
结论:
- 这种新的方法在分布式学习中提供了更好的拜占庭稳定性和沟通效率.
- 波利克势头是减轻噪音和防御拜占庭工人的关键.
- 该算法显示了非凸优化的实际有效性和理论保证.
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