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咖啡馆:通过利用缺失数据异质性来改进联邦数据推算

Sitao Min¹, Hafiz Asif², Xinyue Wang¹

¹Rutgers University, Newark, NJ, USA.

IEEE transactions on knowledge and data engineering

|May 5, 2025

概括

联合学习 (FL) 通过 Cafe 解决了缺失的数据挑战,这是个性化的方法. 咖啡馆提高了归算质量,特别是在异质数据设置中,优于现有方法.

科学领域:

机器学习机器学习
数据科学数据科学数据科学
分散式系统分散式系统分散式系统

背景情况:

联合学习 (FL) 是一种分散的方法,可以提高性能,同时保持数据自主性和机密性.
在FL中处理缺失值是一个尚未探索的领域,特别是在客户端之间有异质数据分布的情况下.
当前最先进的 (SOTA) 联合归算方法在异质环境中表现出显著的性能下降.

研究的目的:

调查缺少数据的联合归算方法,重点关注数据异质性的复杂场景.
解决现有的SOTA方法在数据异质性下保持归算质量的局限性.
提出一种新的个性化联合学习方法,以改善缺失数据的归算.

主要方法:

介绍Cafe,这是一个个性化的联合学习方法,用于缺失数据的归算.
利用客户之间观察到的和缺失的数据分布差异来提高归算质量.
通过计算自动校准权重来开发个性化的归算模型,以适应不同级别的异质性.

主要成果:

咖啡馆在同质数据设置中与SOTA基线性能相匹配.
咖啡馆在异质数据设置中显著优于SOTA基线.
经验评估证实了咖啡馆在各种环境中的有效性.

结论:

咖啡馆为联邦缺失数据归算提供了有效的解决方案,特别是在具有挑战性的异质环境中.
咖啡馆的个性化权重策略适应数据异质性,提高归算准确性.
拟议的方法通过解决关键数据质量问题,推进联合学习领域.

关键词:

数据异质性数据异质性数据质量数据质量联合学习学习 (Federated Learning) 是一种联合学习.缺失的数据归算缺失的数据归算

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