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Federated Deep Unfolding for Sparse Recovery

Mogilipalepu, KK and Modukuri, SK and Madapu, A and Chepuri, SP (2021) Federated Deep Unfolding for Sparse Recovery. In: European Signal Processing Conference, 23 - 27 August 2021, Dublin, pp. 1950-1954.

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Official URL: https://doi.org/10.23919/EUSIPCO54536.2021.9615969


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 the training data distributed across many clients to a central location. 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.

Item Type: Conference Paper
Publication: European Signal Processing Conference
Publisher: European Signal Processing Conference, EUSIPCO
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Data privacy; Deep learning; Economic and social effects; Iterative methods; Learning algorithms; Recovery; Signal reconstruction, Algorithmic unrolling; Algorithmics; Compressed-Sensing; Distributed learning; Federated learning; Layer-wise; Learning techniques; Sparse recovery; Sparse signal recoveries; Unfoldings, Compressed sensing
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 23 May 2023 04:17
Last Modified: 23 May 2023 04:17
URI: https://eprints.iisc.ac.in/id/eprint/81724

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