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.
Communication-efficient learning of deep networks from decentralized data
2 Pith papers cite this work. Polarity classification is still indexing.
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Over-the-air federated learning exceeds amplifier peak-power limits in practice, and clipping-filtering mitigation degrades performance especially in multi-carrier OFDM systems.
citing papers explorer
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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.
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On Signal Peak Power Constraint of Over-the-Air Federated Learning
Over-the-air federated learning exceeds amplifier peak-power limits in practice, and clipping-filtering mitigation degrades performance especially in multi-carrier OFDM systems.