Ghasemkhani Bita, Varliklar Ozlem, Dogan Yunus, Utku Semih, Birant Kokten Ulas, Birant Derya
Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, Turkey.
Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey.
Animals (Basel). 2024 Jul 9;14(14):2021. doi: 10.3390/ani14142021.
Federated learning is a collaborative machine learning paradigm where multiple parties jointly train a predictive model while keeping their data. On the other hand, multi-label learning deals with classification tasks where instances may simultaneously belong to multiple classes. This study introduces the concept of Federated Multi-Label Learning (FMLL), combining these two important approaches. The proposed approach leverages federated learning principles to address multi-label classification tasks. Specifically, it adopts the Binary Relevance (BR) strategy to handle the multi-label nature of the data and employs the Reduced-Error Pruning Tree (REPTree) as the base classifier. The effectiveness of the FMLL method was demonstrated by experiments carried out on three diverse datasets within the context of animal science: Amphibians, Anuran-Calls-(MFCCs), and HackerEarth-Adopt-A-Buddy. The accuracy rates achieved across these animal datasets were 73.24%, 94.50%, and 86.12%, respectively. Compared to state-of-the-art methods, FMLL exhibited remarkable improvements (above 10%) in average accuracy, precision, recall, and F-score metrics.
联邦学习是一种协作式机器学习范式，其中多个参与方在保留各自数据的同时共同训练一个预测模型。另一方面，多标签学习处理的是实例可能同时属于多个类别的分类任务。本研究引入了联邦多标签学习（FMLL）的概念，将这两种重要方法结合起来。所提出的方法利用联邦学习原理来解决多标签分类任务。具体而言，它采用二元相关性（BR）策略来处理数据的多标签性质，并采用简约误差剪枝树（REPTree）作为基础分类器。通过在动物科学背景下的三个不同数据集上进行实验，证明了FMLL方法的有效性：两栖动物数据集、无尾目叫声（MFCCs）数据集和HackerEarth领养伙伴数据集。在这些动物数据集上分别实现的准确率为73.24%、94.50%和86.12%。与现有最先进方法相比，FMLL在平均准确率、精确率、召回率和F值指标上表现出显著提升（超过10%）。
