论文标题
混合联合学习：算法和实施
Hybrid Federated Learning: Algorithms and Implementation
论文作者
论文摘要
联合学习（FL）是最近提出的分布式机器学习范式，用于分布式和私人数据集。基于数据分区模式，FL通常被分为水平，垂直和混合设置。尽管前两种方法已经开发了许多作品，但混合FL设置（涉及部分重叠的特征空间和样品空间）仍然不那么探索，尽管这种设置在实践中极为重要。在本文中，我们首先为Hybrid FL设置了一个新的基于模型匹配的问题表达，然后提出了一种有效的算法，可以协作训练全球和本地模型以处理完整的部分特征数据。我们对多视图ModelNet40数据集进行数值实验，以验证所提出的算法的性能。据我们所知，这是为Hybrid FL开发的第一个配方和算法。
Federated learning (FL) is a recently proposed distributed machine learning paradigm dealing with distributed and private data sets. Based on the data partition pattern, FL is often categorized into horizontal, vertical, and hybrid settings. Despite the fact that many works have been developed for the first two approaches, the hybrid FL setting (which deals with partially overlapped feature space and sample space) remains less explored, though this setting is extremely important in practice. In this paper, we first set up a new model-matching-based problem formulation for hybrid FL, then propose an efficient algorithm that can collaboratively train the global and local models to deal with full and partial featured data. We conduct numerical experiments on the multi-view ModelNet40 data set to validate the performance of the proposed algorithm. To the best of our knowledge, this is the first formulation and algorithm developed for the hybrid FL.
