论文标题
使用可控制的数据摘要来减轻联邦学习中的数据缺失
Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digests
论文作者
论文摘要
缺乏训练数据及其在联合学习（FL）中的分布变化会大大破坏模型的性能，尤其是在跨核心方案中。为了应对这一挑战，我们使用数据摘要（FedDig）框架介绍了联合学习。 FedDig使用新颖的可控制数据摘要表示形式来管理意外的分布更改。该框架使FL用户可以通过操纵控制多个低维特征的混合并将差异隐私扰动对这些混合功能应用于这些混合功能的高度参数来调整摘要的保护水平。在四个不同的公共数据集中对FedDig的评估表明，在各种数据缺席的情况下，它始终优于五种基线算法。我们还彻底探索了Feddig的超参数，证明了其适应性。值得注意的是，FedDig插件设计本质上是可扩展的，并且与现有的FL算法兼容。
The absence of training data and their distribution changes in federated learning (FL) can significantly undermine model performance, especially in cross-silo scenarios. To address this challenge, we introduce the Federated Learning with Data Digest (FedDig) framework. FedDig manages unexpected distribution changes using a novel privacy-controllable data digest representation. This framework allows FL users to adjust the protection levels of the digest by manipulating hyperparameters that control the mixing of multiple low-dimensional features and applying differential privacy perturbation to these mixed features. Evaluation of FedDig across four diverse public datasets shows that it consistently outperforms five baseline algorithms by substantial margins in various data absence scenarios. We also thoroughly explored FedDig's hyperparameters, demonstrating its adaptability. Notably, the FedDig plugin design is inherently extensible and compatible with existing FL algorithms.
