论文标题
联邦学习中的设备异质性：一种超品牌方法
Device Heterogeneity in Federated Learning: A Superquantile Approach
论文作者
论文摘要
我们提出了一个联合学习框架，以处理不符合人群数据分布的异质客户设备。该方法取决于一个基于参数化的超量化目标，其中参数范围范围范围范围。我们提出了一种优化算法，并确定其融合到固定点。我们通过将通常的联合平均方法与设备过滤交织在一起，展示如何使用安全聚合实际实施它。我们以关于神经网络的数值实验以及计算机视觉和自然语言处理的任务的线性模型的结尾。
We propose a federated learning framework to handle heterogeneous client devices which do not conform to the population data distribution. The approach hinges upon a parameterized superquantile-based objective, where the parameter ranges over levels of conformity. We present an optimization algorithm and establish its convergence to a stationary point. We show how to practically implement it using secure aggregation by interleaving iterations of the usual federated averaging method with device filtering. We conclude with numerical experiments on neural networks as well as linear models on tasks from computer vision and natural language processing.
