  Increasing data volumes delivered by a new generation of radio
interferometers require computationally efficient and robust calibration
algorithms. In this paper, we propose distributed calibration as a way of
improving both computational cost as well as robustness in calibration. We
exploit the data parallelism across frequency that is inherent in radio
astronomical observations that are recorded as multiple channels at different
frequencies. Moreover, we also exploit the smoothness of the variation of
calibration parameters across frequency. Data parallelism enables us to
distribute the computing load across a network of compute agents. Smoothness in
frequency enables us reformulate calibration as a consensus optimization
problem. With this formulation, we enable flow of information between compute
agents calibrating data at different frequencies, without actually passing the
data, and thereby improving robustness. We present simulation results to show
the feasibility as well as the advantages of distributed calibration as opposed
to conventional calibration.
