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Generative AI can transform RF-WPT scheduling by enhancing decision-making under uncertainty, leading to more robust energy delivery strategies.
By adapting AI model complexity to channel conditions, this work unlocks significant gains in edge inference throughput compared to fixed-complexity approaches.
Achieve Byzantine-robust, privacy-preserving federated learning without sacrificing convergence speed by letting each device autonomously optimize its neighbor selection using a GNN-RL agent.
