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.Comment: 20 pages, 9 figure