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September 11, 2025Open Access

Federated Learning for Secure Data Sharing Across Distributed Networks

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Authors

LALalitha Anand

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Overview

Federated learning enhances privacy and collaboration in distributed networks, indicating potential for secure data sharing.

Key Points

  • Federated learning significantly enhances privacy while allowing multiple participants to collaboratively train models.
  • By utilizing local data and only sharing model updates, federated learning preserves confidentiality across diverse domains.
  • Recent advances in secure aggregation and differential privacy help mitigate security risks in federated learning environments.
  • Federated learning's scalability and resilience are essential for future decentralized intelligence applications.

Cite This Study

Lalitha Anand (2025) studied this question.

synapsesocial.com/papers/68d41711713b0b5dfea629eahttps://doi.org/10.20944/preprints202509.0828.v1
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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 in the Era of Decentralized Intelligence: Challenges and Opportunities2025 · 2 citations
  2. 2Privacy-Preserving Federated Learning: Challenges, Techniques, and Prospects for Distributed AI2025
  3. 3Federated Learning for Privacy-Preserving Artificial Intelligence: Challenges, Opportunities, and Future Directions2025
  4. 4A Decentralized Approach to Privacy-Preserving Data Analysis using Federated Learning2025 · 1 citations
  5. 5A Systematic Review of Federated Learning: Emerging Techniques, Challenges, and Research Directions2025