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This paper conducts an empirical analysis of developer challenges in Federated Learning (FL) by examining 495 Stack Overflow posts and 9,116 GitHub issues across 92 FL-related projects. The study employs BERTopic-based topic modeling to identify nine key topics on Stack Overflow and thirteen on GitHub, revealing persistent difficulties in areas such as environment setup, API migrations, and training instability with non-IID data. The findings highlight a significant demand for procedural guidance among developers and suggest that existing tools and documentation are insufficient to address these challenges effectively.
Developers face critical pain points in Federated Learning, with unresolved issues in setup and training that could hinder the adoption of this promising technology.
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.