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October 15, 2025International Journal For Multidisciplinary ResearchOpen Access

Privacy-Preserving Federated Learning: Challenges, Techniques, and Prospects for Distributed AI

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Authors

AKAditya KumarMCMahip ChaurasiaRSRanjit Singh

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Overview

Review highlights challenges and techniques of federated learning for privacy in IoT and healthcare applications.

Key Points

  • Federated learning improves privacy by enabling decentralized training without sharing raw data.
  • Key techniques include differential privacy and secure aggregation to enhance data security during model updates.
  • Challenges like data heterogeneity and regulatory compliance hinder the adoption and effectiveness of federated learning.
  • Future directions involve improving communication efficiency and integrating federated learning with IoT and edge environments.

Cite This Study

Kumar et al. (2025) studied this question.

synapsesocial.com/papers/68efbd16d61273c8652d7f93https://doi.org/10.36948/ijfmr.2025.v07i05.57558
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Also Consider

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  1. 1Federated Learning for Privacy-Preserving Artificial Intelligence: Challenges, Opportunities, and Future Directions2025
  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 Architectures For Privacy-Preserving Distributed Machine Learning2023
  5. 5Federated Learning in the Era of Decentralized Intelligence: Challenges and Opportunities2025 · 3 citations