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A Transformer-enhanced Double Deep Q-Network (TDDQN) framework that effectively integrates a Standard Transformer Encoder with a hierarchical global-local map representation for large-scale urban AAV navigation to enable scalable operation and temporal reasoning in complex urban landscapes is proposed.
This work proposes FedHAttn, a novel hierarchical attention–based aggregation mechanism that explicitly models inter-client model feature importance to optimize global model performance and establishes an effective aggregator that balances accuracy, robustness, and efficiency in federated PM2.5 prediction.
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