论文标题
安排用于联合学习的算法，以最少的能耗
Scheduling Algorithms for Federated Learning with Minimal Energy Consumption
论文作者
论文摘要
Federated Learning (FL) has opened the opportunity for collaboratively training machine learning models on heterogeneous mobile or Edge devices while keeping local data private.With an increase in its adoption, a growing concern is related to its economic and environmental cost (as is also the case for other machine learning techniques).Unfortunately, little work has been done to optimize its energy consumption or emissions of carbon dioxide or equivalents, as energy minimization is usually left as a secondary目的。在本文中，我们调查了通过控制工作负载分配来最大程度地限制FL培训的能源消耗的问题。我们将其作为最小成本的FL时间表问题，一个相同的，独立的，独立和原子质的总成本最小化问题，我们必须将其赋予以前的pseudo-pseudo pseudo psseploial nocial nocial。多项选择最低成本最大背包包装问题。我们还为方案提供了四种算法，其中成本功能单调增加并遵循相同的行为。这些解决方案同样适用于其他类型的成本以及其他一维数据分区问题的最小化。
Federated Learning (FL) has opened the opportunity for collaboratively training machine learning models on heterogeneous mobile or Edge devices while keeping local data private.With an increase in its adoption, a growing concern is related to its economic and environmental cost (as is also the case for other machine learning techniques).Unfortunately, little work has been done to optimize its energy consumption or emissions of carbon dioxide or equivalents, as energy minimization is usually left as a secondary objective.In this paper, we investigate the problem of minimizing the energy consumption of FL training on heterogeneous devices by controlling the workload distribution.We model this as the Minimal Cost FL Schedule problem, a total cost minimization problem with identical, independent, and atomic tasks that have to be assigned to heterogeneous resources with arbitrary cost functions.We propose a pseudo-polynomial optimal solution to the problem based on the previously unexplored Multiple-Choice Minimum-Cost Maximal Knapsack Packing Problem.We also provide four algorithms for scenarios where cost functions are monotonically increasing and follow the same behavior.These solutions are likewise applicable on the minimization of other kinds of costs, and in other one-dimensional data partition problems.
