Deploying federated learning at the wireless edge introduces federated edge learning (FEEL). Given FEEL's limited communication resources and potential mislabeled data on devices, improper resource allocation or data selection can hurt convergence speed and increase training costs. Thus, to realize an efficient FEEL system, this paper emphasizes jointly optimizing resource allocation and data selection. Specifically, in this work, through rigorously modeling the training process and deriving an upper bound on FEEL's one-round convergence rate, we establish a problem of joint resource allocation and data selection, which, unfortunately, cannot be solved directly. Toward this end, we equivalently transform the original problem into a solvable form via a variable substitution and then break it into two subproblems, that is, the resource allocation problem and the data selection problem. The two subproblems are mixed-integer non-convex and integer non-convex problems, respectively, and achieving their optimal solutions is a challenging task. Based on the matching theory and applying the convex-concave procedure and gradient projection methods, we devise a low-complexity suboptimal algorithm for the two subproblems, respectively. Finally, the superiority of our proposed scheme of joint resource allocation and data selection is validated by numerical results.

翻译：在无线边缘部署联邦学习催生了联邦边缘学习（FEEL）。考虑到FEEL有限的通信资源以及设备上可能存在的错误标记数据，不当的资源分配或数据选择会损害收敛速度并增加训练成本。因此，为实现高效的FEEL系统，本文强调对资源分配与数据选择进行联合优化。具体而言，在本工作中，我们通过对训练过程进行严格建模并推导出FEEL单轮收敛速率的一个上界，建立了一个资源分配与数据选择的联合优化问题，但该问题无法直接求解。为此，我们通过变量替换将原问题等价转化为可求解形式，并将其分解为两个子问题，即资源分配问题与数据选择问题。这两个子问题分别为混合整数非凸问题与整数非凸问题，获取其最优解是一项具有挑战性的任务。基于匹配理论，并应用凸凹过程与梯度投影方法，我们分别为这两个子问题设计了一种低复杂度的次优算法。最终，数值结果验证了我们所提出的资源分配与数据选择联合优化方案的优越性。