多阶段异步联合学习与自适应差异性隐私
IEEE transactions on pattern analysis and machine intelligence
|November 13, 2023
概括
我们为异步联合学习 (AFL) 与差异隐私 (DP) 引入了新的算法. 我们的方法提高了模型准确性和融合速度,同时保持了分散的AI系统的强有力的隐私保证.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 与差异隐私 (DP) 结合,提供了强大的隐私保护.
- 同步FL由于设备异质性 (滞后效应) 而遭受效率低下.
- 非同步FL (AFL) 减轻了滞后者效应,但缺乏全面的研究,特别是DP.
研究的目的:
- 为了应对DP增强的AFL中公用事业优化的挑战.
- 为DP-AFL开发理论上有动机的多阶段自适应性私有算法.
- 改进异步联合学习中模型实用性和隐私之间的权衡.
主要方法:
- 开发两个PD增强的AFL框架,考虑各种对手模型的通用因素.
- 理论分析AFL模型的融合,以实现具有高功效的自适应DP.
- 在各种培训模型和基准数据集中实施和评估拟议的算法.
主要成果:
- 拟议的算法与最先进的方法相比,显示出更高的性能.
- 在相同的隐私损失水平下,测试准确度提高了24%.
- 在实验评估中表现出更快的收率.
结论:
- 开发的框架为私营AFL提供了分析方法.
- 这些算法有效地平衡了DP增强的AFL中的模型实用性和隐私.
- 提出的方法可以适应更广泛的复杂FL应用场景.
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