← Back to all news
arXiv Machine Learning By Aravind Reddy, Deeraj S K, Mohd Wasif Raza, Saurav Prakash

Machine Unlearning for Federated Socially Fair k-Means Clustering

Read the original on arXiv Machine Learning →

The paper presents a method for exact machine unlearning in federated socially fair k‑means clustering, aiming to maintain fairness across demographic groups while allowing data deletion. It introduces separate local summaries per group so that unaffected institutions can keep valid global fairness weights, and demonstrates that reusing cached computations—especially when an entire client withdraws—can dramatically speed up retraining. Experiments on three datasets show significant speedups for initialization and moderate gains for individual record deletions, highlighting when exact unlearning is most beneficial.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Sep 7

Efficient Clustering with Quality Guardrails for LLM-based Recommender Systems at Industry Scale

The paper presents a scalable two‑stage clustering algorithm that guarantees per‑sample quality guardrails for large‑scale LLM‑based recommender systems. By first forming Mini‑batch K‑Means clusters and then greedily selecting representatives that meet user‑specified similarity and attribute constraints, the method ensures each sample inherits only relevant and safe outputs. Benchmarks show the approach runs faster and scales to millions of inputs, achieving a 50‑fold reduction in downstream LLM cost and runtime while maintaining personalization.

By Longshaokan Wang, Wai Tsang Keung, Punit Ghodasara, Roman Wang, Ali Dashti, Francesc Moreno-Noguer
llmsragbenchmarkssafety
More like this →
arXiv AI
Sep 10

Parameterized and Streaming Algorithms for Euclidean Fair $k$-Center Clustering

The paper presents new algorithms for fair k‑center clustering in Euclidean spaces, where a dataset is divided into groups and each group has a limit on the number of centers that can be chosen. A parameterized approximation algorithm achieves a 2.732 ratio, which is improved to 2.414 with exponential time in k. By integrating this into a one‑pass streaming framework, the authors obtain streaming approximations of 4.464 (improvable to 3.828) and a polynomial‑time streaming algorithm with a 4.732 ratio, further reduced to 4.42, surpassing previous state‑of‑the‑art results. Experiments confirm that these methods outperform existing approaches in clustering accuracy.

By Zeyu Lin, Chaoqi Jia, Longkun Guo, Chao Chen
benchmarkssafety
More like this →