Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining
Year: 2023
Type: conference-paper
Abstract: To address the big data challenges, serverless multi-party collaborative training has recently attracted attention in the data mining community, since they can cut down the communications cost by avoiding the server node bottleneck. However, traditional serverless multi-party collaborative training algorithms were mainly designed for balanced data mining tasks and are intended to optimize accuracy (e.g., cross-entropy). The data distribution in many real-world applications is skewed and classifiers, which are trained to improve accuracy, perform poorly when applied to imbalanced data tasks since models could be significantly biased toward the primary class. Therefore, the Area Under Precision-Recall Curve (AUPRC) was introduced as an effective metric. Although multiple single-machine methods have been designed to train models for AUPRC maximization, the algorithm for multi-party collaborative training has never been studied. The change from the single-machine to the multi-party setting poses critical challenges. For example, existing single-machine-based AUPRC maximization algorithms maintain an inner state for local each data point, thus these methods are not applicable to large-scale multi-party collaborative training due to the dependence on each local data point.
Authors: Xidong Wu, Zhengmian Hu, Jian Pei, Heng Huang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
Awards: Collaborative Research: III: Medium: New Machine Learning Empowered Nanoinformatics System for Advancing Nanomaterial Design, SCH: INT: New Machine Learning Framework to Conduct Anesthesia Risk Stratification and Decision Support for Precision Health, A New Machine Learning Framework for Single-Cell Multi-Omics Bioinformatics, Collaborative Research: CCRI: New: A Scalable Hardware and Software Environment Enabling Secure Multi-party Learning, BIGDATA: IA: Collaborative Research: Asynchronous Distributed Machine Learning Framework for Multi-Site Collaborative Brain Big Data Mining, Collaborative Research: PPoSS: LARGE: Co-designing Hardware, Software, and Algorithms to Enable Extreme-Scale Machine Learning Systems, III: Medium: Collaborative Research: Integrating Large-Scale Machine Learning and Edge Computing for Collaborative Autonomous Vehicles
OpenAlex ID: W4385568099