  Adaptive networks have the capability to pursue solutions of global
stochastic optimization problems by relying only on local interactions within
neighborhoods. The diffusion of information through repeated interactions
allows for globally optimal behavior, without the need for central
coordination. Most existing strategies are developed for cooperative learning
settings, where the objective of the network is common to all agents. We
consider in this work a team setting, where a subset of the agents form a team
with a common goal while competing with the remainder of the network. We
develop an algorithm for decentralized competition among teams of adaptive
agents, analyze its dynamics and present an application in the decentralized
training of generative adversarial neural networks.
