Exploring the under-explored terrain of non-open source data for software engineering through the lens of federated learning

In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. The exploration of effective collaborative training settings, which are capable of leveraging valuable knowledge from distributed and isolated datasets, is increasingly crucial.This study investigates key factors that impact the effectiveness of collaborative training methods in code next-token prediction, as well as the correctness and utility of the generated code, showing the promise of such methods. Additionally, we evaluate the memorization of different participant training data across various collaborative training settings, including centralized, federated, and incremental training, showing their potential risks in leaking data.Our findings indicate that the size and diversity of code datasets are pivotal factors influencing the success of collaborative trained code models. We demonstrate that federated learning achieves competitive performance compared to centralized training while offering better data protection, as evidenced by lower memorization ratios in the generated code. However, federated learning can still produce verbatim code snippets from hidden training data, potentially violating data privacy or copyright. Our study further explores the patterns of effectiveness and memorization in incremental learning, emphasizing the importance of the sequence in which individual participant datasets are introduced. Also, we identify the memorization phenomenon of cross-organizational clones as a prevalent challenge in both centralized and federated learning scenarios. Our findings highlight the persistent risk of data leakage during inference, even when training data remains unseen. We conclude with strategic recommendations for practitioners and researchers to optimize the use of multisource datasets, thereby propelling the cross-organizational collaboration forward.

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Exploring the under-explored terrain of non-open source data for software engineering through the lens of federated learning

Cited by 3 publications

References 33 publications

Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps

Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps

Code Summarization without Direct Access to Code - Towards Exploring Federated LLMs for Software Engineering

Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization

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