Decentralized Clustering and Linking by Networked Agents
This work addresses the challenge of efficient cooperation and inference in decentralized multi-agent systems, representing an incremental improvement by integrating clustering and learning into a single strategy.
The paper tackles the problem of decentralized clustering and estimation in multi-task networks where agents must identify clusters of similar objectives without prior knowledge, proposing an integrated learning and clustering algorithm that achieves exponentially decaying error probabilities with step-size parameter.
We consider the problem of decentralized clustering and estimation over multi-task networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which agents in their neighborhood belong to the same cluster. We propose a decentralized clustering algorithm aimed at identifying and forming clusters of agents of similar objectives, and at guiding cooperation to enhance the inference performance. One key feature of the proposed technique is the integration of the learning and clustering tasks into a single strategy. We analyze the performance of the procedure and show that the error probabilities of types I and II decay exponentially to zero with the step-size parameter. While links between agents following different objectives are ignored in the clustering process, we nevertheless show how to exploit these links to relay critical information across the network for enhanced performance. Simulation results illustrate the performance of the proposed method in comparison to other useful techniques.