We consider a set of learning agents in a collaborative peer-to-peer network,
where each agent learns a personalized model according to its own learning
objective. The question addressed in this paper is: how can agents improve upon
their locally trained model by communicating with other agents that have
similar objectives? We introduce and analyze two asynchronous gossip algorithms
running in a fully decentralized manner. Our first approach, inspired from
label propagation, aims to smooth pre-trained local models over the network
while accounting for the confidence that each agent has in its initial model.
In our second approach, agents jointly learn and propagate their model by
making iterative updates based on both their local dataset and the behavior of
their neighbors. To optimize this challenging objective, our decentralized
algorithm is based on ADMM.

本论文研究了一组学习代理在协作对等网络中，每个代理根据自己的学习目标学习个性化模型。本文介绍和分析了两种异步流言算法，以完全分散的方式运行。我们的第一种方法旨在在网络上平滑预训练的本地模型，同时考虑每个代理在其初始模型中的置信度。在我们的第二种方法中，代理共同学习和传播其模型，基于其本地数据集和邻居的行为进行迭代更新，为了优化这一具有挑战性的目标，我们的分散算法是基于 ADMM。