标题： 低成本的联邦学习设计 显示英文标题

标题： Cost-Effective Federated Learning Design

摘要： 联邦学习（FL）是一种分布式学习范式，使大量设备能够在不共享原始数据的情况下协作学习一个模型。 尽管其在实际应用中具有高效性和有效性，但设备上的迭代学习过程在学习时间和能耗方面产生了相当大的成本，这在很大程度上取决于每轮训练中选择的客户端数量和本地迭代次数。 在本文中，我们分析如何设计自适应FL，以最优地选择这些关键控制变量，从而在确保收敛的前提下最小化总成本。 理论上，我们通过收敛上界，分析性地建立了总成本与控制变量之间的关系。 为了高效解决成本最小化问题，我们开发了一种低成本的基于采样的算法，用于学习与收敛相关的未知参数。 我们推导出重要的解决方案属性，这些属性能够有效识别不同度量偏好下的设计原则。 在实践中，我们在模拟环境和硬件原型上评估了我们的理论结果。 实验证据验证了我们推导的属性，并表明我们的解决方案在各种数据集、不同的机器学习模型以及异构系统设置下都能实现接近最优的性能。 显示英文摘要

摘要： Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process 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 that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. Theoretically, we analytically establish the 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 metric preferences. 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 various datasets, different machine learning models, and heterogeneous system settings.