Federated Learning Frameworks for Secure and Decentralized Authentication

Merlin Balamurugan

doi:10.36948/ijfmr.2024.v06i05.27901

Federated Learning Frameworks for Secure and Decentralized Authentication

Author(s)	Merlin Balamurugan
Country	United States
Abstract	Federated Learning (FL) is a cutting-edge machine learning approach that enables multiple or edge devices to train models collaboratively without sharing sensitive data. This approach not only ensures the privacy and security of data by keeping it localized but also promotes the collective improvement of machine learning models across various participants. A systematic literature review explored integrating Blockchain technology with federated learning. Blockchain's potential to address existing security and privacy vulnerabilities in traditional federated learning systems is analyzed in depth. One of the key benefits of combining Blockchain with FL is enhanced protection against potential attacks, such as data tampering or unauthorized access. The study also examines how Blockchain-based federated learning systems can offer better records and rewards management, contributing to fairer and more transparent systems. In addition, Blockchain's role in improving verification and accountability within federated learning frameworks has been critically evaluated. By integrating Blockchain, federated learning can achieve higher levels of trust and security in collaborative machine-learning processes. The latest research highlights innovative Blockchain-based methods that tackle these challenges, ensuring robust privacy and security measures. Overall, this approach represents a significant advancement in distributed machine learning, aligning with contemporary needs for data protection and collaborative efficiency.
Keywords	Federated Learning, Distributed machine learning, Blockchain, Smart contract, Privacy and Security
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-09-26
Cite This	Federated Learning Frameworks for Secure and Decentralized Authentication - Merlin Balamurugan - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.27901
DOI	https://doi.org/10.36948/ijfmr.2024.v06i05.27901
Short DOI	https://doi.org/g59zvx

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Federated Learning Frameworks for Secure and Decentralized Authentication

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