To leverage rich data distributed at the network edge, a new machine-learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low-latency multi-access scheme for edge learning. To this end, we consider a popular privacy-preserving framework, federated edge learning (FEEL), where a global AI-model at an edge-server is updated by aggregating (averaging) local models trained at edge devices. It is proposed that the updates simultaneously transmitted by devices over broadband channels should be analog aggregated “over-the-air” by exploiting the waveform-superposition property of a multi-access channel. Such broadband analog aggregation (BAA) results in dramatical communication-latency reduction compared with the conventional orthogonal access (i.e., OFDMA). In this work, the effects of BAA on learning performance are quantified targeting a single-cell random network. First, we derive two tradeoffs between communication-and-learning metrics, which are useful for network planning and optimization. The power control (“truncated channel inversion”) required for BAA results in a tradeoff between the update-reliability [as measured by the receive signal-to-noise ratio (SNR)] and the expected update-truncation ratio. Consider the scheduling of cell-interior devices to constrain path loss. This gives rise to the other tradeoff between the receive SNR and fraction of data exploited in learning. Next, the latency-reduction ratio of the proposed BAA with respect to the traditional OFDMA scheme is proved to scale almost linearly with the device population. Experiments based on a neural network and a real dataset are conducted for corroborating the theoretical results.

中文翻译：

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用于低延迟联合边缘学习的宽带模拟聚合

为了利用分布在网络边缘的丰富数据，出现了一种称为边缘学习的新机器学习范式，其中学习算法部署在边缘，为移动用户提供智能服务。在计算速度飞速发展的同时，通信延迟正成为快速边缘学习的瓶颈。为了解决这个问题，这项工作的重点是设计一种用于边缘学习的低延迟多路访问方案。为此，我们考虑了一种流行的隐私保护框架，即联合边缘学习 (FEEL)，其中边缘服务器上的全局 AI 模型通过聚合（平均）在边缘设备上训练的本地模型进行更新。建议通过利用多路访问信道的波形叠加特性，设备通过宽带信道同时传输的更新应通过“空中”模拟聚合。与传统的正交接入（即OFDMA）相比，这种宽带模拟聚合（BAA）可显着降低通信延迟。在这项工作中，针对单细胞随机网络量化了 BAA 对学习性能的影响。首先，我们在通信和学习指标之间得出两个权衡，这对网络规划和优化很有用。BAA 所需的功率控制（“截断信道反转”）导致更新可靠性 [由接收信噪比 (SNR) 衡量] 和预期更新截断率之间的权衡。考虑调度小区内部设备以限制路径损耗。这导致了接收 SNR 和学习中利用的数据部分之间的另一个权衡。接下来，证明所提出的 BAA 相对于传统 OFDMA 方案的延迟减少率几乎与设备数量成线性关系。进行了基于神经网络和真实数据集的实验以验证理论结果。