信息物理网络和社交网络中基于交互的分布式学习
Interaction-Based Distributed Learning in Cyber-Physical and Social Networks
摘要：
在本文中，我们考虑了一个网络场景中，代理可以根据一个分数图，模型一些物理或社会互动的相互评价。我们的目标是设计一个分布式协议，由代理运行，允许他们学习他们的未知状态之间的有限集的可能值。我们提出了一个贝叶斯框架，其中分数和状态分别与未知参数和超参数的概率事件相关联。我们证明，每个代理可以学习它的状态，通过一个本地贝叶斯分类器和（集中）最大似然（ML）估计的参数超参数，结合平原ML和经验贝叶斯方法。通过使用图形模型中的工具，使我们能够深入了解分数和状态的条件依赖性，我们提供了两个宽松的概率模型，最终导致ML参数超参数估计服从分布式计算。为了突出建议放宽的适当性，我们展示了分布式估计的机器对机器的测试设置异常检测和社会互动设置用户分析。
英文摘要：
In this paper we consider a network scenario in which agents can evaluate each other according to a score graph that models some physical or social interaction. The goal is to design a distributed protocol, run by the agents, allowing them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which scores and states are associated to probabilistic events with unknown parameters and hyperparameters respectively. We prove that each agent can learn its state by means of a local Bayesian classifier and a (centralized) Maximum-Likelihood (ML) estimator of the parameter-hyperparameter that combines plain ML and Empirical Bayes approaches. By using tools from graphical models, which allow us to gain insight on conditional dependences of scores and states, we provide two relaxed probabilistic models that ultimately lead to ML parameter-hyperparameter estimators amenable to distributed computation. In order to highlight the appropriateness of the proposed relaxations, we demonstrate the distributed estimators on a machine-to-machine testing set-up for anomaly detection and on a social interaction set-up for user profiling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
