标题： 基于年龄的设备选择与空中联邦学习的发射功率优化 显示英文标题

标题： Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning

摘要： 最近，空中联邦学习（FL）因其能够提高通信效率而受到广泛关注。 然而，空中FL的性能通常受到设备选择策略和信号聚合误差的限制。 特别是，在FL中忽略慢速设备会导致模型更新的公平性下降，并放大全局模型对某些设备数据的偏差，最终影响整体系统性能。 为了解决这个问题，我们提出了一个联合设备选择和发射功率优化框架，确保慢速设备的适当参与，保持高效的训练性能，并保证及时更新。 首先，我们进行理论分析，以量化基于信息时效性（AoI）的设备选择下空中FL的收敛上界。 我们的分析进一步表明，所选设备的数量和信号聚合误差对收敛上界有显著影响。 为了最小化期望加权峰值信息时效性的总和，我们使用Lyapunov优化计算每个通信轮次的设备优先级，并通过贪心算法选择优先级最高的设备。 然后，我们制定并解决一个发射功率和归一化因子优化问题，以最小化时间平均均方误差（MSE）。 实验结果表明，我们提出的方法具有两个显著优势：(1) 与基线方法相比，它降低了MSE并提高了模型性能，以及 (2) 它在公平性和训练效率之间取得平衡，同时保持令人满意的时效性，确保模型性能稳定。 显示英文摘要

摘要： Recently, over-the-air federated learning (FL) has attracted significant attention for its ability to enhance communication efficiency. However, the performance of over-the-air FL is often constrained by device selection strategies and signal aggregation errors. In particular, neglecting straggler devices in FL can lead to a decline in the fairness of model updates and amplify the global model's bias toward certain devices' data, ultimately impacting the overall system performance. To address this issue, we propose a joint device selection and transmit power optimization framework that ensures the appropriate participation of straggler devices, maintains efficient training performance, and guarantees timely updates. First, we conduct a theoretical analysis to quantify the convergence upper bound of over-the-air FL under age-of-information (AoI)-based device selection. Our analysis further reveals that both the number of selected devices and the signal aggregation errors significantly influence the convergence upper bound. To minimize the expected weighted sum peak age of information, we calculate device priorities for each communication round using Lyapunov optimization and select the highest-priority devices via a greedy algorithm. Then, we formulate and solve a transmit power and normalizing factor optimization problem for selected devices to minimize the time-average mean squared error (MSE). Experimental results demonstrate that our proposed method offers two significant advantages: (1) it reduces MSE and improves model performance compared to baseline methods, and (2) it strikes a balance between fairness and training efficiency while maintaining satisfactory timeliness, ensuring stable model performance.