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October 3, 2025International Journal For Multidisciplinary ResearchOpen Access

Federated Learning for Privacy-Preserving Artificial Intelligence: Challenges, Opportunities, and Future Directions

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Authors

SKSandeep Kumar

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Overview

Federated learning improves AI model accuracy while protecting user data, highlighting challenges in secure data transfer and decentralized training.

Key Points

  • Federated learning achieves high model accuracy with enhanced privacy protections, addressing crucial privacy concerns.
  • Experimental results indicate that federated learning can maintain near-centralized accuracy using decentralized model training.
  • Technical challenges like non-IID data and communication bottlenecks are critical to the successful implementation of federated learning.
  • Future research should explore hybrid federated learning-blockchain models to further secure collaborative data processing.

Cite This Study

Sandeep Kumar (2025) studied this question.

synapsesocial.com/papers/68e02f40f0e39f13e7fa2a4dhttps://doi.org/10.36948/ijfmr.2025.v07i05.56495
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Also Consider

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

  1. 1Privacy-Preserving Federated Learning: Challenges, Techniques, and Prospects for Distributed AI2025
  2. 2Securing AI: Federated Learning as a Tool for Privacy Preservation2024 · 6 citations
  3. 3Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence2025 · 3 citations
  4. 4Federated Learning in the Era of Decentralized Intelligence: Challenges and Opportunities2025 · 3 citations
  5. 5Building cross-border federated infrastructures for secure and private AI: an overview of privacy enhancing technologies and their challenges2026