The popular federated edge learning (FEEL) framework allows privacy-preserving collaborative model training via frequent learning-updates exchange between edge devices and server. Due to the constrained bandwidth, only a subset of devices can upload their updates at each communication round. This has led to an active research area in FEEL studying the optimal device scheduling policy for minimizing communication time. However, owing to the difficulty in quantifying the exact communication time, prior work in this area can only tackle the problem partially by considering either the communication rounds or per-round latency, while the total communication time is determined by both metrics. To close this gap, we make the first attempt in this paper to formulate and solve the communication time minimization problem. We first derive a tight bound to approximate the communication time through cross-disciplinary effort involving both learning theory for convergence analysis and communication theory for per-round latency analysis. Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. The effectiveness of the proposed scheme is demonstrated via a use case on collaborative 3D objective detection in autonomous driving.

中文翻译：

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通过优化的概率设备调度加速联合边缘学习

流行的联合边缘学习 (FEEL) 框架允许通过边缘设备和服务器之间频繁的学习更新交换来进行隐私保护的协作模型训练。由于带宽受限，只有一部分设备可以在每一轮通信中上传其更新。这导致了 FEEL 的一个活跃研究领域，研究用于最小化通信时间的最佳设备调度策略。然而，由于难以量化准确的通信时间，该领域的先前工作只能通过考虑通信轮次或每轮延迟来部分解决问题，而总通信时间由这两个指标决定。为了缩小这一差距，我们在本文中首次尝试制定和解决通信时间最小化问题。我们首先通过涉及收敛分析的学习理论和每轮延迟分析的通信理论的跨学科努力推导出一个紧密的界限来近似通信时间。在分析结果的基础上，通过求解近似通信时间最小化问题，以封闭形式导出优化的概率调度策略。发现随着训练过程的发展，优化策略的优先级逐渐从抑制剩余的通信轮次转向减少每轮延迟。通过自动驾驶中协同 3D 目标检测的用例证明了所提出方案的有效性。