Hypernetwork-Aided Unfolded D-Admm: Adaptive Parameterization for Distributed Optimization

This paper presents a model-based deep learning approach to distributed optimization via Unfolded D-ADMM. We address a key limitation of prior unfolding-based approaches: graph-specificity. To overcome this, we use a hypernetwork to dynamically generate algorithm parameters based on the structure of the underlying communication graph. This hypernetwork is conditioned on graph embeddings extracted by a Graph Neural Network encoder. The result is a generalizable Unfolded D-ADMM model that adapts to varying graph topologies without retraining. We demonstrate this approach on the distributed LASSO task and validate its performance on both seen and unseen topologies.