GOAMP achieves error-free reconstruction of sublinearly sparse signals from linear measurements when the measurement dimension exceeds a threshold matching that of Gaussian AMP, provided the non-zero support avoids a neighborhood of the origin.
A fast iterative shrinkage-th resholding algorithm for linear inverse problems,
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The paper introduces an energy-efficient federated edge learning framework that quantifies learning loss from sample counts, applies stochastic online adaptation, and solves resource optimization with convergence bounds to improve performance in IoT networks.
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Generalized Orthogonal Approximate Message-Passing for Sublinear Sparsity
GOAMP achieves error-free reconstruction of sublinearly sparse signals from linear measurements when the measurement dimension exceeds a threshold matching that of Gaussian AMP, provided the non-zero support avoids a neighborhood of the origin.
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Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks
The paper introduces an energy-efficient federated edge learning framework that quantifies learning loss from sample counts, applies stochastic online adaptation, and solves resource optimization with convergence bounds to improve performance in IoT networks.