Communication-reducing diffusion LMS algorithm over multitask networks

Many practical problems in the filed of distributed estimation happen to be multi-task oriented. Without prior knowledge of clustering structure, i.e. nodes do not know which clusters they belong to beforehand, distributed algorithms for parameter estimation have received great attention in recent years. In most previous work, each node collaborates with all its neighboring nodes at each iteration, which introduces unnecessary communication assumption when any node cooperates with neighboring nodes from different clusters. In this paper, we propose a novel communication-reducing diffusion LMS (Least-Mean-Square) algorithm, called the CR-dLMS algorithm, for estimating true parameters in multi-task environment. Under the CR-dLMS algorithm, we control the probabilities of data fusion from neighboring nodes by minimizing mean-square-deviation (MSD) to reduce communication cost among nodes. Theoretical analysis for the learning behavior of the CR-dLMS algorithm is performed, and simulation results show that the CR-dLMS algorithm can indeed achieve the same estimation performance as several other previous algorithms while great reducing the communication cost among nodes.