【联邦元学习】论文解读：Federated Meta-Learning for Fraudulent Credit Card Detection

论文：Zheng W, Yan L, Gou C, et al. Federated Meta-Learning for Fraudulent Credit Card Detection[C], Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Special Track on AI in FinTech. Pages 4654-4660. 2020: 4654-4660.

【原创，转载需标明出处】论文解析（内含论文原文）：https://ripe-heliotrope-6f4.notion.site/Federated-Meta-Learning-for-Fraudulent-Credit-Card-Detection-81403861597a40678994d5e1e6f1f7fb

2020年发表在IJCAI的这篇联邦元学习的论文，主要在元学习过程中加入度量学习来对Query Set进行二分类，提出了一种新的联邦元学习框架—deep K-tuplet network，推广了triplet网络允许与k个负样本进行联合比较。通过Feature Extraction Model进行度量学习出特征，通过Relation Model进行相似度匹配。