LipKernel parameterizes dissipative convolution kernels via 2-D Roesser state-space models so that layer-wise LMIs enforce network Lipschitz bounds while allowing standard fast convolution evaluation after training.
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An optimal convex-reformulated power control algorithm is derived for signal-level integrated sensing, computing and communication in AirComp-based federated learning under a joint target detection constraint.
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LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
LipKernel parameterizes dissipative convolution kernels via 2-D Roesser state-space models so that layer-wise LMIs enforce network Lipschitz bounds while allowing standard fast convolution evaluation after training.
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Analytically Characterized Optimal Power Control for Signal-Level-Integrated Sensing, Computing and Communication in Federated Learning
An optimal convex-reformulated power control algorithm is derived for signal-level integrated sensing, computing and communication in AirComp-based federated learning under a joint target detection constraint.