Federated Learning for Secure Data Sharing in Distributed Cybersecurity Networks
Keywords:
Federated learning, cybersecurityAbstract
As cybersecurity threats become increasingly sophisticated, organizations face significant challenges in sharing sensitive data securely across distributed networks. Traditional centralized data sharing methods expose sensitive information to potential breaches, leading to privacy concerns and compliance issues. Federated learning (FL) offers a promising solution by enabling organizations to collaboratively train machine learning models while keeping their data decentralized. This paper investigates the potential of federated learning for secure and privacy-preserving data sharing across distributed cybersecurity networks. It discusses the underlying principles of federated learning, its advantages in enhancing data privacy, and real-world applications in cybersecurity. Additionally, the paper addresses the challenges associated with implementing federated learning, including model convergence, communication overhead, and security risks. The findings suggest that federated learning can significantly enhance secure data sharing while mitigating the risks associated with centralized data storage.
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References
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