一句话：研究代码大语言模型的训练数据记忆模式，并探索联邦学习与差分隐私以缓解隐私泄露。
代码专用大语言模型（LLM4Code）已能辅助代码生成、调试和测试，但集中式训练数据上的记忆现象会带来机密与知识产权风险，导致模型往往只能使用公开数据。该研究考察代码记忆模式，并探索联邦学习与差分隐私等隐私保护技术来缓解这些风险。目标是开发安全、高性能且包容的模型，以保护敏感信息、促进信任并推动其在软件工程中的更广泛采用。摘要未报告具体实验结果。
Recent advances in code-specialized language models (LLM4Code) have revolutionized software development by assisting with tasks like code generation, debugging, and testing. However, training these models on large, centralized datasets raises privacy concerns, as memorization of training data can lead to confidentiality and intellectual property risks. As a result, such models are often trained on publicly available data. This research examines code memorization patterns and explores privacy-preserving techniques, such as Federated Learning and Differential Privacy, to mitigate these risks. The goal is to develop secure, high-performance, and inclusive models that protect sensitive information, foster trust, and enable broader adoption in software engineering.