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September 23, 2025International Journal of Computing and Engineering

Federated Machine Learning Across Hybrid Clouds: Balancing Security and Privacy

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Authors

SDSri Ramya Deevi

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Overview

This research reveals key trade-offs in implementing federated machine learning, balancing privacy preservation and security in hybrid cloud infrastructures.

Key Points

  • Federated learning in hybrid clouds enhances privacy while presenting challenges related to security and compliance.
  • Key performance metrics include model accuracy, training efficiency, and regulatory compliance in federated scenarios.
  • Investigating techniques like differential privacy and secure multiparty computation indicates scalability and efficiency issues.
  • The proposed framework aids organizations in navigating trust management and protecting sensitive data within hybrid infrastructures.

Cite This Study

Sri Ramya Deevi (2023) studied this question.

synapsesocial.com/papers/68d4768331b076d99fa6ef49https://doi.org/10.47941/ijce.3191
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Federated learning for privacy-preserving, secure and scalable data intelligence in hybrid cloud systems2026
  2. 2Data Privacy in Federated Learning: The Trade-Offs in Balancing Operational Performance with Confidentiality2026
  3. 3Privacy Protection Optimization in Federated Learning2025
  4. 4Enhancing Privacy In Federated Learning: A Comprehensive Survey Of Preservation Techniques2026
  5. 5Federated Learning and Data Privacy in Distributed Machine Learning2026