用同态加密解决联邦学习中的类别不平衡问题，保护隐私同时提升模型精度。
On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning
- 客户端用CKKS加密共享数据属性，私有化平衡本地数据集。
- 在多个主流数据集上，相比基线方法显著提升联邦学习准确率。
- 适合注重隐私保护且数据分布不均的联邦学习场景。
训练数据中的类别不平衡会导致机器学习模型产生偏差并泛化能力差。虽然在集中式学习中可通过预处理有效缓解这些问题，但在联邦学习等分布式环境中检测和解决此类问题极具挑战。本文提出FLICKER，一种基于同态加密的隐私保护框架，用于应对联邦学习中的全局类别不平衡问题。核心在于利用流行的CKKS同态加密方案，使客户端可私密共享数据属性，并在实施联邦学习前先平衡本地数据集。大量实验结果表明，该方法在多个主流数据集与基准方法上均显著提升了联邦学习的准确率。
原文摘要 · Abstract (English)
Class imbalance in training datasets can lead to bias and poor generalization in machine learning models. While pre-processing of training datasets can efficiently address both these issues in centralized learning environments, it is challenging to detect and address these issues in a distributed learning environment such as federated learning. In this paper, we propose FLICKER, a privacy preserving framework to address issues related to global class imbalance in federated learning. At the heart of our contribution lies the popular CKKS homomorphic encryption scheme, which is used by the clients to privately share their data attributes, and subsequently balance their datasets before implementing the FL scheme. Extensive experimental results show that our proposed method significantly improves the FL accuracy numbers when used along with popular datasets and relevant baselines.
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