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走向高效的联合学习:分层修剪量化方案和编码设计
Zheqi Zhu1,2, Yuchen Shi1,2, Gangtao Xin1,2
1Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
联合学习 (FL) 的效率得到了FedLP-Q的提升,这是一个新的层次智能修剪量化框架. 这种方法减少了分布式系统中的通信计算瓶,性能损失最小.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 是一种分布式机器学习范式,可以实现分散的培训.
- 实际FL部署遇到重要的通信计算瓶,阻碍了效率.
- 现有的FL优化方法往往缺乏统一的修剪和量化方法.
研究的目的:
- 提出FedLP-Q,一种新的联合学习,具有层次智能的修剪-定量化方案.
- 解决FL系统中的通信计算瓶问题.
- 为FL优化开发一个通用和明确的框架.
主要方法:
- 开发了FedLP-Q,这是FL的分层修剪量化框架.
- 为同质和异质场景设计了特定的修剪策略.
- 实现了一个随机量化规则和相应的编码方案.
主要成果:
- FedLP-Q在通信和计算方面证明了系统效率的提高.
- 拟议的方案实现了可控的性能退化.
- 理论和实验评估验证了FedLP-Q.的有效性.
结论:
- 对于FL系统,FedLP-Q提供了一个联合修剪-量子化解决方案.
- 层级处理使其在实际FL部署中易于应用.
- 这一框架有效地缓解了联合学习中的通信计算瓶.
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