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适应性批量大小 时间演变 随机梯度 下降 对于联合学习
IEEE transactions on pattern analysis and machine intelligence
|September 15, 2025
概括
联合适应性批量大小时间演变变异减少 (FedATEVR) 通过优化大批量大小和减少梯度噪声来改善联合学习. 这提高了分布式机器学习系统的准确性和通信效率.
科学领域:
- 机器学习 机器学习
- 分布式系统 分布式系统
- 优化算法 优化算法
背景情况:
- 差异减小技术在集中式设置中增强了随机梯度下降 (SGD).
- 联合学习 (FL) 在应用差异减少时面临诸如超大批量大小,梯度噪声和统计异质性等挑战.
研究的目的:
- 提出一个轻量级算法,FedATEVR,解决联合学习中的差异减少问题.
- 提高联合学习系统的效率和准确性.
主要方法:
- 开发了一个适应性批量大小方案,使用客户的历史梯度信息.
- 引入了一个随时间演变的减差梯度估计器,根据梯度差异调整权重.
- 该算法集成了全球和本地梯度信息,以稳定大批量大小.
主要成果:
- 理论上证明了O ((1/sqrt ((SKT)) 的线性加速度,用于部分客户参与的非凸起的目标.
- 与基线方法相比,经验证明了优越的测试准确性.
- 显著减少了融合所需的沟通轮次数.
结论:
- FedATEVR有效地解决了将差异减少应用于联合学习的关键挑战.
- 拟议的方法为加快联合SGD和降低计算成本提供了一个实际的解决方案.
- 在联合学习场景中,在准确性和沟通效率方面取得了实质性的改进.
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