المفاهيم الأساسية
Federated learning can enable collaborative development and maintenance of open-source AI-based software engineering tools while preserving data privacy and enhancing model performance.
الملخص
The paper discusses the opportunities and challenges of developing open-source AI-based software engineering tools. It highlights the current limitations of open-source code model development, such as limited access to high-quality data, lack of strong community support, and inefficient resource utilization.
To address these challenges, the paper proposes a decentralized governance framework for open-source code models based on federated learning (FL). This framework allows multiple entities, including research labs, industry organizations, and companies, to collaboratively train and maintain code models while preserving data privacy.
The key aspects of the proposed framework include:
Developer guidelines covering data protocols, model architecture, updating strategies, and version control.
A governance committee to manage the community and review new participant contributions.
The use of federated learning to enable collaborative model training without data sharing, ensuring privacy protection.
The paper also presents a comprehensive experimental evaluation to assess the impact of data heterogeneity on the performance of federated learning models across various code-related tasks, such as clone detection, defect detection, code search, code-to-text, and code completion. The results demonstrate the potential of federated learning to achieve performance comparable to centralized training while preserving data privacy.
The paper concludes by discussing the challenges and opportunities in implementing this decentralized governance framework for open-source AI-based software engineering tools, including code privacy protection, reward mechanisms, collaborative interaction protocols, copyright issues, and security concerns.
الإحصائيات
The performance of federated learning models is closely aligned with centralized training in specific scenarios, such as fine-tuning large language models for code completion tasks.
Federated learning can outperform single-client training in code-related tasks, highlighting the benefits of collaborative learning while preserving data privacy.
Data heterogeneity, particularly imbalances in label distribution, can impact model performance in federated learning settings.
اقتباسات
"Federated learning safeguards data privacy and compliance, and significantly enhances AI model performance through collaborative modeling."
"Our experimental results strongly supports the potential use of federated learning in bringing together various companies to collaborate on the development of intelligent software engineering, thereby promoting the advancement of this field."