Machine Unlearning for Federated Socially Fair k-Means Clustering
Federated clustering can support applications such as patient stratification across hospitals and customer segmentation across distributed financial datasets.
ProofPaper ↗
Key points
- 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.
- 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.
Sources (1)
- [1]Machine Unlearning for Federated Socially Fair k-Means ClusteringarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 03:16 PM
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.
Extractive summary: sentences quoted from the sources.
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