在异构数据中实现鲁棒个性化均值估计，抗干扰能力强。
Robust Federated Personalised Mean Estimation for the Gaussian Mixture Model
- 针对高斯混合模型设计鲁棒个性化均值估计算法
- 误差与异常样本比例近似线性增长，性能逼近理论下界
- 适合存在恶意客户端的联邦学习场景
联邦学习中的异构数据与个性化问题受到广泛关注。同时，针对联邦学习中数据被污染的鲁棒性也已有研究。本文探讨将个性化与鲁棒性结合，在存在恒定比例被污染客户端的情况下进行建模。基于此，我们提出一个简化实例，聚焦于高斯混合模型下的个性化均值估计问题。所提算法的误差几乎随被污染样本占比线性增长，并给出了具有相同行为的下界，仅存在常数因子差距。
原文摘要 · Abstract (English)
Federated learning with heterogeneous data and personalization has received significant recent attention. Separately, robustness to corrupted data in the context of federated learning has also been studied. In this paper we explore combining personalization for heterogeneous data with robustness, where a constant fraction of the clients are corrupted. Motivated by this broad problem, we formulate a simple instantiation which captures some of its difficulty. We focus on the specific problem of personalized mean estimation where the data is drawn from a Gaussian mixture model. We give an algorithm whose error depends almost linearly on the ratio of corrupted to uncorrupted samples, and show a lower bound with the same behavior, albeit with a gap of a constant factor.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。