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基于符号的梯度下降与异质数据:融合和拜占庭弹性
IEEE transactions on neural networks and learning systems
|January 12, 2024
概括
多数投票的SignSGD是沟通高效的,但在联合学习中与数据异质性作斗争. 一个新型的大小驱动压缩机确保了数据差异的趋同,增强了深度学习培训.
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:
- 通信开支是分布式深度神经网络培训中的一个关键瓶.
- 多数投票的SignSGD提供了通信效率和拜占庭的稳定性.
- SignSGD的融合受阻于数据异质性,这种异质性在联合学习中普遍存在.
研究的目的:
- 为解决由于数据异质性而导致SignSGD在联合学习中的非融合问题.
- 开发一种新的梯度压缩方法,用于基于符号的随机梯度下降.
- 建立理论上的融合保证和量化拜占庭的弹性.
主要方法:
- 导出一个足够的条件来实现基于符号的梯度下降的收.
- 基于大小驱动的随机信号基梯度压缩机的建议.
- 整合一个错误反机制以提高学习绩效.
主要成果:
- 提出的方法在存在任意数据异质性的情况下实现了趋同.
- 量化了基于符号的梯度下降方法的拜占庭弹性.
- 在MNIST,CIFAR-10和Tiny-ImageNet数据集上的实验验证证明了它的有效性.
结论:
- 这种新型的大小驱动压缩机有效地解决了SignSGD在异质联合学习环境中的非融合问题.
- 该方法保持了对拜占庭袭击的稳健性,同时提高了学习性能.
- 这项工作促进了高效可靠的分布式深度学习培训.
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