examine the feasibility of an innovative solution that combines Federated Learning with Deep Reinforcement Learning (DRL) to optimize the selection of edge nodes for model aggregation. Through the proposed DRL-based client selection method, the system can actively select nodes that have shown superior performance based on their training quality, computational resources, and network conditions. This […]
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