发表机构
University of Saskatchewan（萨斯喀彻温大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究通过分析Stack Overflow帖子和GitHub问题，利用主题建模等方法，刻画联邦学习开发者痛点，包括环境设置等多方面问题，并按问题意图分类，为框架设计者等提供建议，给出持续监测痛点及改进系统的可扩展方法。
AI 中文摘要
联邦学习（FL）能在不集中原始数据的情况下进行协作模型训练，但构建和运行FL系统仍很困难。本文通过独立分析495个Stack Overflow帖子以及9116个来自92个FL相关项目的GitHub问题和拉取请求，对FL开发者面临的挑战进行实证研究。利用基于BERTopic的主题建模和未解决率、中位解决时间等难度指标，刻画反复出现的问题领域并比较在两个平台的表现。分析得出九个主要的Stack Overflow主题和十三个GitHub主题，持续的困难集中在环境设置等方面。还按问题意图对帖子分类，发现“How”类问题居多。一些主题未解决率高、解决时间长，反映出工具、文档和调试支持的不足。基于这些发现为相关人员提供了可行建议，该研究提供了持续监测开发者痛点及改进FL系统可用性等的可扩展方法。
英文摘要
Federated Learning (FL) enables collaborative model training without centralizing raw data, but building and operating FL systems remains difficult due to distributed execution, rapidly evolving frameworks, and privacy and governance requirements. In this paper, we present an empirical study of FL developer challenges by independently analyzing 495 Stack Overflow posts and 9,116 GitHub issues and pull requests from 92 FL-related projects. Using BERTopic-based topic modeling and difficulty indicators such as unresolved rates and median resolution time, we characterize recurring problem areas and compare how they manifest across the two support platforms, Stack Overflow and GitHub. Our analysis surfaces nine dominant Stack Overflow topics and thirteen GitHub topics, with persistent difficulties concentrated in environment setup and dependency compatibility, API breakages and migration, training instability under non-IID data, evaluation and metric correctness, and the integration of privacy-preserving mechanisms. We also categorize posts by question intent to understand the kinds of help developers seek; this intent analysis shows that "How"-type questions dominate, reflecting strong demand for procedural guidance. Several topics, such as "TFF Installation and Environment Compatibility" and "Federated Feature Engineering and SecureBoost Issues," exhibit high unresolved rates and long resolution times, suggesting shortcomings in tooling, documentation, and debugging support. Based on these findings, we provide actionable implications for FL framework designers, documentation authors, and educators. Although our results are constrained to public discussions and a subset of widely discussed frameworks, the study offers a scalable method for continuously monitoring developer pain points and improving the usability, reliability, and deployability of FL systems.
Comments44 pages
