Decentralized Cybersecurity: Implementing Federated Learning in Threat Intelligence Networks

Abstract

Federated Learning (FL), a decentralized machine learning approach, enables companies to exchange threat data. One study discovered that federated learning enhances cybersecurity and facilitates the sharing of threat information. Federated Learning develops a collective model in a decentralized manner with local data. Data is safeguarded, allowing for corporate interaction.

The FL model consolidates sensitive data locally. Federated Learning consolidates model alterations rather than raw data to ensure data privacy. Threat intelligence and modifications to the FedAvg algorithm are examined. The advantages of FL's cybersecurity regarding the confidentiality and integrity of crucial data are analyzed.

FL improves the detection and response capabilities of the threat information exchange program. Examples demonstrate how FL frameworks utilize threat data to recognize and address emerging threats. FL's installations demonstrate the establishment of a collaborative cybersecurity ecosystem that enables enterprises to share threat information while safeguarding data confidentiality.

Implementing practical federated learning is challenging. The convergence of models and communication overhead impede the performance of federated learning systems. This study enhances communication protocols, minimizes model updates, and converges models through sophisticated aggregation techniques. Federated transfer learning and differential privacy modifications may enhance and expand federated learning in collaborative threat intelligence systems.

Federated Learning for collaborative threat information exchange has been thoroughly examined. Illustrations and case examples illustrate the advantages of FL. Technological challenges and resolutions may assist Florida cybersecurity researchers and professionals. FL has the potential to enhance cybersecurity by rendering threat intelligence more secure and collaborative.