论文标题
交谈或工作：灵活的沟通压缩，用于通过异质移动边缘设备进行节能的联合学习
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices
论文作者
论文摘要
机器学习，无线通信和移动硬件技术的最新进展有希望地使联合学习（FL）在大型移动边缘设备上，为众多智能移动应用程序打开了新的视野。尽管有潜在的好处，但由于定期的全球同步和持续的本地培训，FL对参与设备施加了巨大的沟通和计算负担，这给电池限制的移动设备带来了巨大挑战。在这项工作中，我们旨在提高FL在移动边缘网络上的能源效率，以适应异质的参与设备而无需牺牲学习绩效。为此，我们开发了一种融合保证的FL算法，从而实现了灵活的通信压缩。在派生的融合结合的指导下，我们设计了一种压缩控制方案，以平衡本地计算（即“工作”）和无线通信（即“谈话”）的能源消耗。特别是，为适应其计算和通信环境的FL参与者而精心选择了压缩参数。使用各种数据集进行了广泛的模拟来验证我们的理论分析，结果还证明了该方案在节能中的功效。
Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications. Despite the potential benefits, FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training, raising great challenges to battery constrained mobile devices. In this work, we target at improving the energy efficiency of FL over mobile edge networks to accommodate heterogeneous participating devices without sacrificing the learning performance. To this end, we develop a convergence-guaranteed FL algorithm enabling flexible communication compression. Guided by the derived convergence bound, we design a compression control scheme to balance the energy consumption of local computing (i.e., "working") and wireless communication (i.e., "talking") from the long-term learning perspective. In particular, the compression parameters are elaborately chosen for FL participants adapting to their computing and communication environments. Extensive simulations are conducted using various datasets to validate our theoretical analysis, and the results also demonstrate the efficacy of the proposed scheme in energy saving.
