Privacy-Preserving Federated Learning for Secure Cloud-Based Data Collaboration

Abstract

Federated learning enables collaborative model training across multiple parties without exposing sensitive data. However, privacy and security concerns remain significant barriers to its adoption in cloud environments. This paper presents a privacy-preserving federated learning framework that incorporates differential privacy and secure multiparty computation (SMPC). The proposed framework ensures that data remains encrypted during training and that individual contributions are anonymized. Performance evaluations demonstrate that the framework achieves high model accuracy while preserving data privacy and confidentiality. The study highlights the potential of federated learning to enable secure and compliant data collaboration in cloud-based environments.