分析开发者在联邦学习中遇到的痛点，揭示工具链和文档短板。
Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub
- 通过分析495个Stack Overflow和9116个GitHub问题，识别出核心开发难题。
- 环境配置、API兼容性、训练不稳等问题解决耗时长且未解决率高。
- 适合框架设计者、文档撰写者及教育者参考，提升系统可用性。
联邦学习（FL）允许多方协作训练模型而无需集中原始数据，但构建和运维FL系统仍面临分布式执行、框架快速迭代以及隐私与治理要求等挑战。本文通过对92个相关项目中的495篇Stack Overflow帖子和9116个GitHub问题与合并请求进行独立分析，采用BERTopic主题建模及未解决率、平均解决时间等难度指标，刻画了常见的问题领域，并对比了两个支持平台的表现差异。研究发现，九个主要的Stack Overflow话题与十三个关键的GitHub话题集中在环境搭建与依赖兼容性、API破坏与迁移、非同分布数据下的训练不稳定性、评估与指标准确性，以及隐私保护机制集成等方面。意图分析显示‘如何’类问题占主导，反映对操作指导的强烈需求。如‘TFF安装与环境兼容性’和‘联邦特征工程与SecureBoost问题’等主题存在高未解决率和长解决周期，表明工具链、文档和调试支持存在不足。基于此，本文为框架设计者、文档编写者和教育者提供了可操作建议。尽管研究仅覆盖公开讨论及部分主流框架，但仍提供了一种持续监测开发者痛点的可扩展方法，有助于提升联邦学习系统的可用性、可靠性和可部署性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。