论文标题
具有战略网络设计的分布式机器学习：游戏理论观点
Distributed Machine Learning with Strategic Network Design: A Game-Theoretic Perspective
论文作者
论文摘要
本文考虑了一个游戏理论框架，用于网络上的分布式机器学习问题，在该网络中，将节点上的信息采集建模为播放器的合理选择。在拟议的游戏中，玩家决定学习参数和网络结构。 NASH均衡表征了本地绩效与学识渊博的分类器的全球协议之间的权衡。我们首先引入了一种交换方法，该方法具有联合学习过程，该过程将每个节点和网络形成的迭代学习集成。我们表明，我们的游戏等同于无向网络的环境中的广义潜在游戏。我们研究拟议的交换算法的融合，分析由游戏确定的网络结构，并与固定网络上的标准分布式学习相比，显示了社会福利的改善。为了使我们的框架适应流数据，我们得出了一个分布式的卡尔曼过滤器。还引入了基于在线镜下降算法的并发算法以整体方式解决NASH平衡。在案例研究中，我们使用帕金森氏病的远程监控来证实结果。
This paper considers a game-theoretic framework for distributed machine learning problems over networks where the information acquisition at a node is modeled as a rational choice of a player. In the proposed game, players decide both the learning parameters and the network structure. The Nash equilibrium characterizes the tradeoff between the local performance and the global agreement of the learned classifiers. We first introduce a commutative approach which features a joint learning process that integrates the iterative learning at each node and the network formation. We show that our game is equivalent to a generalized potential game in the setting of undirected networks. We study the convergence of the proposed commutative algorithm, analyze the network structures determined by our game, and show the improvement of the social welfare in comparison with standard distributed learning over fixed networks. To adapt our framework to streaming data, we derive a distributed Kalman filter. A concurrent algorithm based on the online mirror descent algorithm is also introduced for solving for Nash equilibria in a holistic manner. In the case study, we use telemonitoring of Parkinson's disease to corroborate the results.
