Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process (e.g., local computations and global communications with the server) incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL in mobile edge networks that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. We establish the analytical relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different optimization metrics. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for different optimization metrics for various datasets and heterogeneous system and statistical settings.

中文翻译：

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移动边缘网络中经济高效的联邦学习


联邦学习（FL）是一种分布式学习范式，它使大量移动设备能够在中央服务器的协调下协作学习模型，而无需共享其原始数据。尽管其实际效率和有效性很高，但迭代式设备上学习过程（例如，本地计算和与服务器的全局通信）在学习时间和能源消耗方面会产生相当大的成本，这在很大程度上取决于所选客户端的数量和每轮训练中的局部迭代次数。在本文中，我们分析了如何在移动边缘网络中设计自适应FL，以最佳方式选择这些基本控制变量，以最大限度地降低总成本，同时确保收敛。我们建立了总成本和控制变量之间具有收敛上限的解析关系。为了有效地解决成本最小化问题，我们开发了一种基于低成本采样的算法来学习收敛相关的未知参数。我们得出了重要的解决方案属性，可以有效地识别不同优化指标的设计原则。实际上，我们在模拟环境和硬件原型上评估我们的理论结果。实验证据验证了我们导出的属性，并证明我们提出的解决方案针对各种数据集、异构系统和统计设置的不同优化指标实现了接近最佳的性能。