Distributed Clustering for Secure Multitask Estimation Based on Adaptive Task Switching

Abstract

Consider adversarial multi-task networks with a high cost of sharing genuine information, where agents feed others unreliable information according to a time-varying probability caused by a high privacy cost. This makes agents in the network tend to be selfish, and it greatly increases the difficulty of effective information sharing and distributed multi-task estimation without the prior cluster information. To address these problems, we propose an adaptive task-switching strategy that is governed by our formulated information sharing model, which cooperates with the proposed DATSLCS-AM algorithm. This distributed algorithm guides agents to cooperate reasonably following reputation updating and pseudo-clustering, which can improve the effectiveness of information sharing and also increase the security of estimation over adversarial multi-task networks. The theoretical analysis of the adaptive pseudo-clustering threshold is provided. Finally, extensive simulations validate the superior performance of our distributed algorithm.