This paper considers a game-theoretic framework for distributed learning problems over networks where communications between nodes are costly. In the proposed game, players decide both the learning parameters and the network structure for communications. The Nash equilibrium characterizes the tradeoff between the local performance and the global agreement of the learned classifiers. We introduce a two-layer algorithm to find the equilibrium. The algorithm 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 symmetric networks. We study the convergence of the proposed algorithm, analyze the network structures determined by our game, and show the improvement of the social welfare in comparison with the distributed learning over non-strategic networks. In the case study, we deal with streaming data and use telemonitoring of Parkinson's disease to corroborate the results.

中文翻译：

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基于战略网络的通信高效分布式机器学习：一种两层博弈方法

本文考虑了一个博弈论框架，用于解决节点之间通信成本高昂的网络上的分布式学习问题。在提议的游戏中，玩家决定学习参数和网络结构以进行通信。纳什均衡表征了局部性能和学习分类器的全局一致性之间的权衡。我们引入了一个两层算法来寻找平衡点。该算法采用联合学习过程，将每个节点的迭代学习和网络形成相结合。我们表明，我们的游戏等效于对称网络设置中的广义潜在游戏。我们研究了所提出算法的收敛性，分析了我们的游戏确定的网络结构，并显示与非战略网络上的分布式学习相比，社会福利的改善。在案例研究中，我们处理流数据并使用帕金森病的远程监测来证实结果。