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Top-k data selection via distributed sample quantile inference

1 December 2022
Xu Zhang
M. Vasconcelos
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Abstract

We consider the problem of determining the top-kkk largest measurements from a dataset distributed among a network of nnn agents with noisy communication links. We show that this scenario can be cast as a distributed convex optimization problem called sample quantile inference, which we solve using a two-time-scale stochastic approximation algorithm. Herein, we prove the algorithm's convergence in the almost sure sense to an optimal solution. Moreover, our algorithm handles noise and empirically converges to the correct answer within a small number of iterations.

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