Minimizing communication cost is a fundamental problem in large-scale federated sensor networks. Maintaining model-based views of data streams has been highlighted because it permits efficient data communication by transmitting parameter values of models, instead of original data streams. We propose a framework that employs the advantages of using model-based views for communication-efficient stream data processing over federated sensor networks, yet it significantly improves state-of-the-art approaches. The framework is generic and any time-parameterized models can be plugged, while accuracy guarantees for query results are ensured throughout the large-scale networks. In addition, we boost the performance of the framework by the coded model update that enables efficient model update from one node to another. It predetermines parameter values for the model, updates only identifiers of the parameter values, and compresses the identifiers by utilizing bitmaps. Moreover, we propose a correlation model, named coded inter-variable model, that merges the efficiency of the coded model update with that of correlation models. Empirical studies with real data demonstrate that our proposal achieves substantial amounts of communication reduction, outperforming state-of-the art methods.