BOBA:具有标签偏的拜占庭-强大的联合学习
Wenxuan Bao1, Jun Wu1, Jingrui He1
1University of Illinois Urbana-Champaign.
概括
我们介绍BOBA, 一种联合学习聚合方法, 解决非ID数据中的标签偏差. 提高了对拜占庭袭击的强度,减少了阶级偏见,
科学领域:
- 机器学习
- 分布式系统
- 网络安全
背景情况:
- 联合学习 (FL) 通常假定在客户端之间独立且相同分布的 (IID) 数据.
- 现有的强有力的聚合规则 (AGR) 往往是针对 IID 设置而设计的,并且与非 IID 数据,特别是标签倾斜性相斗争.
- 客户只拥有少数类别的数据,这带来了诸如选择偏差和对佛罗里达州拜占庭攻击的脆弱性等挑战.
研究的目的:
- 在标签歪曲的非IID数据分布下开发一个强大的聚合方法.
- 解决非IID设置中的当前AGR固有的选择偏差和对拜占庭式攻击的增强脆弱性.
- 提出一种高效且理论上可靠的方法,提高所有数据类别的模型性能和公平性.
主要方法:
- 我们提出BOBA (平衡和优化拜占庭意识聚合),一种高效的两阶段聚合方法.
- BOBA旨在减轻选择偏差,并增强非IID联合学习中对抗拜占庭对手的强度.
- 理论分析证实了BOBA与最佳误差边界的融合.
主要成果:
- 经验评估表明,BOBA显著减少了标签偏差引起的代表性不足的阶级的绩效下降.
- 与非IID设置中的最先进的AGR相比,BOBA显示出对拜占庭式攻击的优越稳定性.
- 该方法实现了无偏见的聚合,并在各种数据集和模型中保持高准确性.
结论:
- BOBA有效地解决了标签偏差和联合学习中的拜占庭式攻击的挑战.
- 提出的方法为建立更强大,更公平的联合模式提供了实用和高效的解决方案.
- 未来的工作可以探索BOBA扩展到其他形式的数据异质性.
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