Federated learning frameworks for privacy-preserving collaborative software development

  • Authors

    • Dr. Priya Natarajan Associate Professor, University of Madras, India. Author
    • Dr. Shalini Gupta Associate Professor, Panjab University, India. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2020PI6H3N

    Published 06-05-2020

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

    Issue

    Vol. 3 No. 1 (2020)

    Section

    Articles

    How to Cite

    [1]
    P. Natarajan and S. Gupta, “Federated learning frameworks for privacy-preserving collaborative software development”, IJAIDT, vol. 3, no. 1, pp. 01–12, Jun. 2020, doi: 10.67228/30713315/IJAIDT-2020PI6H3N.

  • Abstract

    Collaborative software development often involves multiple distributed teams working on shared codebases, raising significant privacy concerns related to proprietary code, intellectual property, and sensitive development data. Traditional centralized methods for collaborative model training risk exposing confidential information. This paper proposes a federated learning framework tailored for privacy-preserving collaborative software development, enabling multiple parties to collaboratively train machine learning models on their local code data without sharing raw data. We integrate privacy-enhancing techniques such as differential privacy and secure aggregation to mitigate information leakage. Experimental results demonstrate that the proposed approach achieves competitive model performance while ensuring strong privacy guarantees, offering a practical solution for privacy-sensitive collaborative development environments. The framework lays the groundwork for secure and efficient cooperation in distributed software engineering workflows.

  • References

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