Federated Learning for Decentralized Cybersecurity: Collaborative Defense Against Emerging Threats
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Abstract
The decentralized nature of modern networks requires innovative approaches to cybersecurity that can address threats without compromising sensitive data. This paper explores federated learning as a collaborative defense mechanism, allowing organizations to share threat intelligence and model improvements while keeping their data localized. By training AI models across multiple decentralized environments, the framework enhances the collective understanding of emerging threats. Experimental results demonstrate improved detection rates and adaptability of models in response to new attack vectors, illustrating the potential of federated learning in advancing cybersecurity strategies.
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