Imagine a machine learning agent deployed at each station in a sensor network, so that it can analyze incoming data and determine when something interesting happens. Traditionally, this analysis would be done independently at each station. But what if each agent could talk to its neighbors and find out what they're seeing? We've developed a learning system that enables collaboration so that the agents can autonomously (without human input) improve their performance. Each agent can ask its neighbors for their opinions, then use them to refine its own results. We've evaluated this approach to learning for both classification and clustering. Our target application domain involves the analysis of seismic and infrasonic data collected by the Mount Erebus Volcano Observatory to better understand different types of volcanic activity.