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Making Old Things New: A Unified Algorithm for Differentially Private Clustering
Max Dupr\'e la Tour, Monika Henzinger, David Saulpic
https://arxiv.org/abs/2406.11649

Making Old Things New: A Unified Algorithm for Differentially Private Clustering
As a staple of data analysis and unsupervised learning, the problem of private clustering has been widely studied under various privacy models. Centralized differential privacy is the first of them, and the problem has also been studied for the local and the shuffle variation. In each case, the goal is to design an algorithm that computes privately a clustering, with the smallest possible error. The study of each variation gave rise to new algorithms: the landscape of private clustering algorit…

This https://arxiv.org/abs/2406.05481 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Joint Cooperative Clustering and Power Control for Energy-Efficient Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning
In this paper, we investigate the amalgamation of cell-free (CF) and extremely large-scale multiple-input multiple-output (XL-MIMO) technologies, referred to as a CF XL-MIMO, as a promising advancement for enabling future mobile networks. To address the computational complexity and communication power consumption associated with conventional centralized optimization, we focus on user-centric dynamic networks in which each user is served by an adaptive subset of access points (AP) rather than al…