A VoL/TLW-based PDQN scheduler for NOMA federated meta-learning outperforms DDPG, OMA, equal-weight, and random baselines in simulation.
Client s election and cost-efficient joint optimization for NOMA-enabled hie rarchical federated learning,
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Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems
A VoL/TLW-based PDQN scheduler for NOMA federated meta-learning outperforms DDPG, OMA, equal-weight, and random baselines in simulation.