Federated Deep Unfolding for Sparse Recovery

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 wit...

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Bibliographic Details
Published inarXiv.org
Main Authors Mogilipalepu, Komal Krishna, Modukuri, Sumanth Kumar, Madapu, Amarlingam, Sundeep Prabhakar Chepuri
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 23.10.2020
Subjects
Algorithms
Clients
Iterative methods
Machine learning
Signal reconstruction
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Summary: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.
ISSN:2331-8422