Better Knowledge Enhancement for Privacy-Preserving Cross-Project Defect Prediction
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Abstract (arXiv)
Cross-Project Defect Prediction (CPDP) poses a non-trivial challenge to construct a reliable defect predictor by leveraging data from other projects, particularly when data owners are concerned about data privacy. In recent years, Federated Learning (FL) has become an emerging paradigm to guarantee privacy information by collaborative training a global model among multiple parties without sharing raw data. While the direct application of FL to the CPDP task offers a promising solution to address privacy concerns, the data heterogeneity arising from proprietary projects across different companies or organizations will bring troubles for model training. In this paper, we study the privacy-preserving cross-project defect prediction with data heterogeneity under the federated learning framework. To address this problem, we propose a novel knowledge enhancement approach named FedDP with two simple but effective solutions: 1. Local Heterogeneity Awareness and 2. Global Knowledge Distillation. Specifically, we employ open-source project data as the distillation dataset and optimize the global model with the heterogeneity-aware local model ensemble via knowledge distillation. Experimental results on 19 projects from two datasets demonstrate that our method significantly outperforms baselines.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 64 references that are in the map. First posted on arXiv 23 Dec 2024.
Builds on
17 of 64 references.
References · 17
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5 more
- 2018Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Studentscited by 50
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Citing papers per year: 2025: 1.
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