Federated Learning Frameworks for Collaborative Software Development That Protect Privacy

Adeyemi Praise

Federated Learning Frameworks for Collaborative Software Development That Protect Privacy

Adeyemi Praise
Ladoke Akintola University of Technology

Abstract

When people who are not in the same place work together to make software, they often work on the same codebase. That makes me very worried about the privacy of private code, sensitive development data, and intellectual property. Previously, centralized methods for training cooperative models would allow private data to leak out. This research proposes a federated learning framework enabling multiple organizations to collaboratively train machine learning models on their proprietary code data while ensuring data privacy. To prevent information leakage, we adopt privacy-enhancing methods such as secure aggregation and differential privacy. Experimental results demonstrated that the suggested method is a good fit for privacy-valued collaborative development settings, since it works as well as other models and protects privacy at the same time. It allows remote software engineering teams to cooperate safely and efficiently.

Keywords

Federated Learning, Privacy-Preserving, Collaborative Software Development, Differential Privacy, Secure Aggregation, Distributed Machine Learning, Software Engineering, Data Privacy.

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