Adaptive clustering based on element-wised distance for distributed estimation over multi-task networks

In multitask networks, neighboring agents that belong to different clusters pursue different goals, and therefore arbitrary cooperation will lead to a degradation in estimation performance. In this paper, an adaptive clustering method is proposed for distributed estimation that enables agents to distinguish between subneighbors that belong to the same cluster and those that belong to a different cluster. This creates an appropriate degree of cooperation to improve parameter estimation accuracy, especially for the case where the prior information of a cluster is unknown. In contrast to the static and quantitative threshold that is imposed in traditional clustering methods, we devise a method for real-time clustering hypothesis detection, which is constructed through the use of a reliable adaptive clustering threshold as reference and the averaged element-wise distance between tasks as real-time clustering detection statistic. Meanwhile, we relax the clustering conditions to maintain maximum cooperation without sacrificing accuracy. Simulations are presented to compare the proposed algorithm and some traditional clustering strategies in both stationary and nonstationary environments. The effects of task difference on performance are also obtained to demonstrate the superiority of our proposed clustering strategy in terms of accuracy, robustness, and suitability.