Distributed adaptive clustering learning over time-varying multitask networks

Abstract

With increasing research on distributed processing in networks, adaptive learning strategies have gradually attracted researchers’ attention. The traditional adaptive learning strategy mainly aims at a single task unchanged over time, while real networks often entail multitasks scenarios with tasks that change over time. Furthermore, although cooperation among agents is beneficial for single task time-invariant networks, agents’ indiscriminate cooperation in time-varying multitask networks may cause undesirable effects. In this paper, an adaptive clustering learning approach based on an event-triggered scheme over time-varying multitask networks is proposed. With this process, agents are enabled to distinguish their clusters to determine cooperative neighbors and improve the accuracy of estimation over networks. The mean-square of the proposed algorithm is analyzed in detail, and the error probability of false alarms and false detections of the clustering mechanism is evaluated. An extensive simulation is given to validate the analytical performance of the distributed learning strategy over time-varying multitasks.