# Federated Deep Unfolding for Sparse Recovery

arXiv: https://arxiv.org/abs/2010.12616 · HTML: https://mlsanity.com/paper/2010.12616/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM](https://mlsanity.com/paper/2309.14353/) (2023)
- [Improving Federated Aggregation with Deep Unfolding Networks](https://mlsanity.com/paper/2306.17362/) (2023)
- [Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets](https://mlsanity.com/paper/2407.17999/) (2024)

## Abstract (arXiv)

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.