面向联邦社会公平k均值聚类的机器遗忘
Machine Unlearning for Federated Socially Fair k-Means Clustering
AI总结：
该研究针对联邦社会公平k均值聚类，提出精确机器遗忘方法，证明其在客户端退出时重放比重新训练更快，明确了精确遗忘复用计算的适用场景。
摘要：
联邦聚类可支持跨医院的患者分层、跨分布式金融数据集的客户细分等应用。负责任的人工智能要求人口统计群体的公平代表性，并支持数据删除，部分动机来自GDPR的被遗忘权等权利。这些目标相互作用：删除记录会改变群体间的平衡，并改变剩余人口的聚类结果。我们研究联邦社会公平k均值聚类的精确机器遗忘，其目标是最小化最大群体平均到最近中心的平方距离。我们的方法为每个群体保留独立的局部摘要，因此当全局公平权重变化时，未受影响机构的摘要仍然有效。对于两个群体，我们证明在理想采样下存在与客户端群体比例无关的初始化近似保证，且相同种子的重放可精确复现重新训练。在整个客户端退出时，重放的收益最大：在三个数据集上，仅初始化模型的重放比重新训练快35.8倍，在进行1轮和2轮公平优化（可降低最差群体的成本）后，重放仍分别快2.4倍和1.7倍。对于单个记录删除，收益较小（约1.06倍），且验证缓存分配的成本可能超过其节省的部分。这些结果明确了精确遗忘可在哪些场景下有效复用计算。代码和可复现材料可在该https URL获取。
英文摘要：
Federated clustering can support applications such as patient stratification across hospitals and customer segmentation across distributed financial datasets. Responsible AI calls for fair representation of demographic groups and support for data deletion, motivated in part by rights such as the GDPR's right to erasure. These goals interact: deleting records can change the balance between groups and alter the clustering of the remaining population. We study exact machine unlearning for federated socially fair $k$-means, whose objective is to minimize the largest group-average squared distance to the nearest center. Our approach keeps separate local summaries for each group, so summaries at unaffected institutions remain valid as global fairness weights change. For two groups, we prove an initialization approximation guarantee independent of client group proportions under ideal sampling, and we show that same-seed replay reproduces retraining exactly. Reuse pays off most when a whole client withdraws: across three datasets, replay is $35.8\times$ faster than retraining for the initialization-only model and remains $2.4\times$ and $1.7\times$ faster with one and two fair-refinement rounds, which improve worst-group cost. For individual record deletions, gains are modest (about $1.06\times$), and certifying cached assignments can cost more than it saves. These results map where exact unlearning can profitably reuse computation. Code and reproducibility materials are available at https://github.com/arareddy/mufsc.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
