论文标题
通过基础对齐和体重惩罚改善联合关系数据建模
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty
论文作者
论文摘要
近年来，联邦学习（FL）引起了越来越多的关注。作为一个保护隐私的协作学习范式，它可以实现更广泛的应用程序，尤其是用于计算机视觉和自然语言处理任务。但是，迄今为止，关于关系数据的联邦学习的研究有限，即知识图（KG）。在这项工作中，我们提出了图形神经网络算法的修改版本，该版本在不同参与者的KGS上执行联合建模。具体而言，为了解决算法融合的固有数据异质性问题和效率低下，我们提出了一种新颖的优化算法，称为fedAlign，具有1）最佳运输（OT），以实现客户的个性化和2）重量约束以加快融合的速度。已经在几个广泛使用的数据集上进行了广泛的实验。经验结果表明，我们提出的方法的表现优于最先进的FL方法，例如FedAvg和FedProx，具有更好的收敛性。
Federated learning (FL) has attracted increasing attention in recent years. As a privacy-preserving collaborative learning paradigm, it enables a broader range of applications, especially for computer vision and natural language processing tasks. However, to date, there is limited research of federated learning on relational data, namely Knowledge Graph (KG). In this work, we present a modified version of the graph neural network algorithm that performs federated modeling over KGs across different participants. Specifically, to tackle the inherent data heterogeneity issue and inefficiency in algorithm convergence, we propose a novel optimization algorithm, named FedAlign, with 1) optimal transportation (OT) for on-client personalization and 2) weight constraint to speed up the convergence. Extensive experiments have been conducted on several widely used datasets. Empirical results show that our proposed method outperforms the state-of-the-art FL methods, such as FedAVG and FedProx, with better convergence.
