Title: Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems Show Chinese title

Title: 面向联邦边缘学习系统的空中计算梯度和信道感知动态调度算法

Abstract: To satisfy the expected plethora of computation-heavy applications, federated edge learning (FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency and privacy-preserving. To further improve the efficiency of wireless data aggregation and model learning, over-the-air computation (AirComp) is emerging as a promising solution by using the superposition characteristics of wireless channels. However, the fading and noise of wireless channels can cause aggregate distortions in AirComp enabled federated learning. In addition, the quality of collected data and energy consumption of edge devices may also impact the accuracy and efficiency of model aggregation as well as convergence. To solve these problems, this work proposes a dynamic device scheduling mechanism, which can select qualified edge devices to transmit their local models with a proper power control policy so as to participate the model training at the server in federated learning via AirComp. In this mechanism, the data importance is measured by the gradient of local model parameter, channel condition and energy consumption of the device jointly. In particular, to fully use distributed datasets and accelerate the convergence rate of federated learning, the local updates of unselected devices are also retained and accumulated for future potential transmission, instead of being discarded directly. Furthermore, the Lyapunov drift-plus-penalty optimization problem is formulated for searching the optimal device selection strategy. Simulation results validate that the proposed scheduling mechanism can achieve higher test accuracy and faster convergence rate, and is robust against different channel conditions. Show Chinese abstract

Abstract: 为了满足计算密集型应用的预期需求，联邦边缘学习（FEEL）作为一种具有分布式学习特性的新范式，旨在提供低延迟和隐私保护的能力。 为了进一步提高无线数据聚合和模型学习的效率，在无线信道叠加特性基础上，空域计算（AirComp）作为一种有前景的解决方案正在兴起。 然而，无线信道的衰落和噪声会导致基于AirComp的联邦学习中出现聚合失真。 此外，收集的数据质量以及边缘设备的能量消耗也可能影响模型聚合的准确性和效率，同时影响收敛性。 为了解决这些问题，本文提出了一种动态设备调度机制，该机制可以根据适当的功率控制策略选择合格的边缘设备来传输它们的本地模型，从而通过AirComp在联邦学习中参与服务器端的模型训练。 在此机制中，数据的重要性由本地模型参数的梯度、信道条件以及设备的能量消耗共同衡量。 特别是，为了充分利用分布式数据集并加速联邦学习的收敛速度，未选中设备的本地更新也会被保留并累积以供未来潜在传输，而不是直接丢弃。 此外，还针对寻找最优设备选择策略的问题，构建了Lyapunov漂移加惩罚优化问题。 仿真结果验证了所提出的调度机制能够实现更高的测试精度和更快的收敛速度，并且对不同的信道条件具有鲁棒性。