A closed-form FL convergence upper bound incorporating sensing SNR, dataset size, and transmission reliability enables joint optimization of sensing power, snapshots, and communication power in ISAC systems.
Rectifier nonlinearities improve neural network acoustic models
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ISAC for AI: A Trade-off Framework Across Data Acquisition and Transfer in Federated Learning
A closed-form FL convergence upper bound incorporating sensing SNR, dataset size, and transmission reliability enables joint optimization of sensing power, snapshots, and communication power in ISAC systems.