Federated Learning (FL) enables multiple devices to collaboratively train a shared model while ensuring data privacy. The selection of participating devices in each training round critically affects both the model performance and training efficiency, especially given the vast heterogeneity in training capabilities and data distribution across devices. To address these challenges, we introduce a novel device selection solution called FedRank, which is an end-to-end, ranking-based approach that is pre-trained by imitation learning against state-of-the-art analytical approaches. It not only considers data and system heterogeneity at runtime but also adaptively and efficiently chooses the most suitable clients for model training. Specifically, FedRank views client selection in FL as a ranking problem and employs a pairwise training strategy for the smart selection process. Additionally, an imitation learning-based approach is designed to counteract the cold-start issues often seen in state-of-the-art learning-based approaches. Experimental results reveal that \model~ boosts model accuracy by 5.2\% to 56.9\%, accelerates the training convergence up to $2.01 \times$ and saves the energy consumption up to $40.1\%$.

FedRank是一种新颖的设备选择解决方案，它是一种端到端的基于排名的方法，通过模仿学习预训练来解决数据和系统异构性，从而自适应地选择最适合进行模型训练的客户端。实验结果显示，FedRank提高了模型准确性5.2%至56.9%，加快了训练收敛速度，并节约了40.1%的能量消耗。

基于排名的仿真学习客户选择用于高效的联邦学习