Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.
翻译:联邦学习(FL)使移动设备能够合作学习共同的预测模型,同时在当地保留数据。然而,实际在移动设备上部署FL存在两大研究挑战:(一) 大规模梯度相对于有限频谱资源的频繁无线更新,以及(二) 培训期间的能量饥饿FL通信和本地计算与电池限制的移动设备。为了应对这些挑战,我们在本文件中提出了一个新的多比特超空计算方法(M-AirComp),用于在FL中以频谱高效方式汇总本地模型更新,并进一步提出移动设备高能效FL设计。具体地说,设计并纳入了高精度数字调制版计划,允许移动设备同时在多接入频道选定位置上上上上传模型更新。此外,我们从理论上分析了我们的FL算法的趋同特性。在FL趋同分析的指导下,我们制定了联合传输概率和本地计算控制优化,目的是最大限度地减少总体能源消耗(e. 迭接本地计算+多轮通信) 。具体说,设计并纳入M-AirComComComm,允许移动设备在FL中以模拟方式显示现有节能效率的模拟方法。