Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach
This work addresses distributed clustering for linear models in networks, offering incremental improvements in efficiency for applications like sensor networks or social data analysis.
The paper tackles the problem of improving model estimation efficiency and identifying subgroup memberships in network data by proposing a tree-based fused-lasso penalty to reduce computation and communication costs, with results showing outperformance in estimation accuracy, speed, and cost.
In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the network. A tree-based $l_1$ penalty is proposed to save the computation and communication cost. We design a decentralized generalized alternating direction method of multiplier algorithm for solving the objective function in parallel. The theoretical properties are derived to guarantee both the model consistency and the algorithm convergence. Thorough numerical experiments are also conducted to back up our theory, which also show that our approach outperforms in the aspects of the estimation accuracy, computation speed and communication cost.