Decentralized Security Mechanisms for AI-Driven Wireless Networks : Integrating Blockchain and Federated Learning
DOI:
https://doi.org/10.32628/IJSRSET24105474Keywords:
Blockchain, Federated Learning, Decentralized Security, Data Privacy, Healthcare CybersecurityAbstract
This dissertation explores how blockchain and federated learning can be wedded to create decentralized security measures for AI‑driven wireless networks, with a keen eye on pressing issues like data privacy and integrity. It dives into plenty of quantitative checks – looking at network weaknesses and how current security setups perform – and even throws in some unexpected case comparisons of these technologies in action. In most cases the findings show that this new security layout greatly boosts network resilience and keeps data more confidential, as evidenced by better data integrity and less unauthorized access. A big part of the discussion centers on the health care sector, where keeping patient data safe and meeting strict privacy rules really matters. Generally speaking, by proving that these methods can work in real‑world health care scenarios, the study not only grows our knowledge of cybersecurity in AI‑powered networks but also introduces a fresh framework for upping data security in sensitive settings. The wider implications hint that using decentralized security measures could streamline operations in healthcare systems and, in turn, nurture more trust between patients and providers—ultimately leading to improved patient outcomes and overall satisfaction.
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Flow Diagram of Data Analysis and Applications in Smart Technologies, 2025. [Online]. Available: https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41598-024-51578-7/MediaObjects/41598_2024_51578_Fig1_HTML.png
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