We consider the problem of cooperative distributed estimation within a network of heterogeneous agents. We begin with the situation where each agent observes an independent stream of Bernoulli random variables, and the goal is for each to determine its own Bernoulli parameter. However, the agents of the population can be categorized into a small number of subgroups, where within each group the agents all have identical Bernoulli parameters. We present an algorithm for cooperative estimation in this setting which allows each agent's estimate to asymptotically converge to the correct value. We show how our technique can be applied in other settings, such as in heterogeneous least mean squares filter populations. Finally, we present simulation results showing the benefit of our technique, and compare it to noncooperative parameter estimation in a Bernoulli population.