Sparse Distributed Learning Based on Diffusion Adaptation
This work addresses distributed learning for networks where sparsity is a bottleneck, offering an incremental improvement through regularization.
The paper tackles the problem of distributed estimation over adaptive networks by proposing diffusion LMS strategies that exploit sparsity in the system model, showing that the method outperforms unregularized diffusion versions under certain conditions and provides advantages for sparse data recovery in simulations.
This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to enhance the detection of sparsity via a diffusive process over the network. The resulting algorithms endow networks with learning abilities and allow them to learn the sparse structure from the incoming data in real-time, and also to track variations in the sparsity of the model. We provide convergence and mean-square performance analysis of the proposed method and show under what conditions it outperforms the unregularized diffusion version. We also show how to adaptively select the regularization parameter. Simulation results illustrate the advantage of the proposed filters for sparse data recovery.