our proposed algorithm is exhibited in terms of heterogeneous agent sensing models and noise statistics. In practice, different types of devices (agents) in the network may have very different “sensing quality”. For instance, with state estimation in smart grids, phasor measurement units (PMUs) can have much smaller variance than standard sensing devices. Owing to the high-dimensionality of the state vector and limited storage and processing capabilities in the individual entities of a large-scale CPS, exchanging high-dimensional estimates may be undesirable. Hence, in this paper, we consider a distributed estimator where each agent only infers a fraction of the field, but through cooperation and under the appropriate conditions generates provably consistent estimates of this fraction of the field. In existing distributed estimation schemes such as in [@kar2011convergence; @kar2013consensus+; @Mesbahi-parameter; @Giannakis-est; @Sayed-LMS; @Stankovic-parameter; @Giannakis-LMS; @Nedic-parameter; @sahu2016distributed], the global model information in terms of the sensing models of all the agents are assumed to be inaccessible for any agent. However, the aforementioned setups subsume the knowledge of the dimension of the state vector to be estimated and hence adapt the storage requirements at each agent to cater to the exact dimension of the state vector. In contrast, in this paper, we present a 