A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks
This work addresses the reliability of federated learning frameworks for developers and users, providing foundational insights for bug detection and repair tools, though it is incremental as it builds on existing bug analysis methods applied to a new domain.
The study conducted the first empirical analysis of bugs in open-source federated learning frameworks, manually collecting and classifying 1,119 bugs from 17 frameworks to identify symptoms, root causes, and fix patterns.
Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing clients' data privacy. It has gained substantial popularity in recent years, especially since the enforcement of data protection laws and regulations in many countries. To foster the application of FL, a variety of FL frameworks have been proposed, allowing non-experts to easily train ML models. As a result, understanding bugs in FL frameworks is critical for facilitating the development of better FL frameworks and potentially encouraging the development of bug detection, localization and repair tools. Thus, we conduct the first empirical study to comprehensively collect, taxonomize, and characterize bugs in FL frameworks. Specifically, we manually collect and classify 1,119 bugs from all the 676 closed issues and 514 merged pull requests in 17 popular and representative open-source FL frameworks on GitHub. We propose a classification of those bugs into 12 bug symptoms, 12 root causes, and 18 fix patterns. We also study their correlations and distributions on 23 functionalities. We identify nine major findings from our study, discuss their implications and future research directions based on our findings.