Federated Deep Unfolding for Sparse Recovery

TL;DR

This paper introduces Fed-CS, a federated deep unfolding method for sparse signal recovery that preserves data privacy and reduces communication costs by training models across distributed clients without central data aggregation.

Contribution

It develops a layer-wise federated learning approach for deep unfolding algorithms applied to sparse recovery, enhancing privacy and communication efficiency.

Findings

Fed-CS achieves comparable accuracy to centralized methods.

The approach reduces communication overhead significantly.

Demonstrates effectiveness on synthetic and real datasets.

Abstract

This paper proposes a federated learning technique for deep algorithm unfolding with applications to sparse signal recovery and compressed sensing. We refer to this architecture as Fed-CS. Specifically, we unfold and learn the iterative shrinkage thresholding algorithm for sparse signal recovery without transporting to a central location, the training data distributed across many clients. We propose a layer-wise federated learning technique, in which each client uses local data to train a common model. Then we transmit only the model parameters of that layer from all the clients to the server, which aggregates these local models to arrive at a consensus model. The proposed layer-wise federated learning for sparse recovery is communication efficient and preserves data privacy. Through numerical experiments on synthetic and real datasets, we demonstrate Fed-CS's efficacy and present various trade-offs in terms of the number of participating clients and communications involved compared to a centralized approach of deep unfolding.