Learning Based Distributed Tracking

TL;DR

This paper introduces data-dependent algorithms for distributed tracking that leverage data distribution knowledge to significantly reduce communication costs, with theoretical guarantees and extensive empirical validation.

Contribution

It proposes novel data-dependent algorithms for distributed tracking that improve communication bounds and adapt to data distribution, including learning-based methods.

Findings

Achieved a communication cost of O(k log log N), outperforming the previous O(k log N/k) bound.

Algorithms perform at least 20% of the communication cost of state-of-the-art methods.

Extensive experiments validate the efficiency and robustness of the proposed algorithms.

Abstract

Inspired by the great success of machine learning in the past decade, people have been thinking about the possibility of improving the theoretical results by exploring data distribution. In this paper, we revisit a fundamental problem called Distributed Tracking (DT) under an assumption that the data follows a certain (known or unknown) distribution, and propose a number data-dependent algorithms with improved theoretical bounds. Informally, in the DT problem, there is a coordinator and k players, where the coordinator holds a threshold N and each player has a counter. At each time stamp, at most one counter can be increased by one. The job of the coordinator is to capture the exact moment when the sum of all these k counters reaches N. The goal is to minimise the communication cost. While our first type of algorithms assume the concrete data distribution is known in advance, our second type of algorithms can learn the distribution on the fly. Both of the algorithms achieve a communication cost bounded byO(k log log N) with high probability, improving the state-of-the-art data-independent bound O(k log N/k). We further propose a number of implementation optimisation heuristics to improve both efficiency and robustness of the algorithms. Finally, we conduct extensive experiments on three real datasets and four synthetic datasets. The experimental results show that the communication cost of our algorithms is as least as 20% of that of the state-of-the-art algorithms.