We consider the problem of classification with a (peer-to-peer) network of heterogeneous and partially informative agents, each receiving local data generated by an underlying true class, and equipped with a classifier that can only distinguish between a subset of the entire set of classes. We propose an iterative algorithm that uses the posterior probabilities of the local classifier and recursively updates each agent's local belief on all the possible classes, based on its local signals and belief information from its neighbors. We then adopt a novel distributed min-rule to update each agent's global belief and enable learning of the true class for all agents. We show that under certain assumptions, the beliefs on the true class converge to one asymptotically almost surely. We provide the asymptotic convergence rate, and demonstrate the performance of our algorithm through simulation with image data and experimented with random forest classifiers and MobileNet.

翻译：我们研究了在异构且部分信息智能体（点对点）网络中的分类问题，每个智能体接收由潜在真实类别生成的本地数据，并配备一个仅能区分整个类别集中某个子集的分类器。我们提出了一种迭代算法，该算法利用本地分类器的后验概率，基于本地信号和来自邻居的信念信息，递归更新每个智能体对所有可能类别的局部信念。随后，我们采用一种新颖的分布式最小规则来更新每个智能体的全局信念，并使所有智能体能够学习真实类别。我们证明，在特定假设下，对真实类别的信念以渐近几乎必然的方式收敛到一。我们提供了渐近收敛速率，并通过图像数据仿真展示了我们算法的性能，实验中使用了随机森林分类器和MobileNet。