mlsanityData to 7 Oct 2026

Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub

arXiv 2607.19621 ↗First posted 21 Jul 2026cs.SE cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 91 references that are in the map. First posted on arXiv 21 Jul 2026.

Builds on

26 of 91 references.

References · 26

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  5. 2016Deep Learning with Differential Privacycited by 2,682
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  7. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  8. 2018Federated Learning with Non-IID Datacited by 1,099
  9. 2019Towards Federated Learning at Scale: System Designcited by 826
  10. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  11. 2020The Future of Digital Health with Federated Learningcited by 625
  12. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
14 more
  1. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  2. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  3. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  4. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  5. 2017Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a surveycited by 90
  6. 2019Taxonomy of Real Faults in Deep Learning Systemscited by 90
  7. 2019Peer-to-peer Federated Learning on Graphscited by 85
  8. 2025The European Union general data protection regulation: what it is and what it meanscited by 30
  9. 2013Integrating Document Clustering and Topic Modelingcited by 9
  10. 2022Understanding Quantum Software Engineering Challenges An Empirical Study on Stack Exchange Forums and GitHub Issuescited by 4
  11. 2013Developer Experience: Concept and Definitioncited by 3
  12. 2026Analyzing GitHub Issues and Pull Requests in nf-core Pipelines: Insights into nf-core Pipeline Repositoriescited by 2
  13. 2024Federated Learning: Attacks, Defenses, Opportunities, and Challengescited by 1
  14. 2026Maintenance and Support in Community-Driven Scientific Pipeline Ecosystems: A Cross-Platform Empirical Study of nf-corecited by 1

Built on by

Not cited yet.