Citation :

Faraz Ahmed, 2024. "EdgeSecFL: A Lightweight Federated Learning Model for Intrusion Detection in IoT-Cloud Ecosystems", ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 4: 163-176.

Abstract :

As IoT devices continue to proliferate and integrate with cloud computing platforms, the attack surface for cyber threats has expanded significantly. The paper is a review of EdgeSecFL framework, which is a light federated learning (FL) model introduced to deal with prevention of intrusion in the IoT-cloud ecosystem. EdgeSecFL uses decentralized training, secure aggregation and blockchain-based logging to achieve greater privacy, cut down on communication overhead, and establish tamper-evident auditability. The model supports adaptive edge coordination and cost-efficient cloud deployment, making it suitable for resource-constrained environments. Comparative analysis proves that EdgeSecFL is superior to previous FL-based IDS systems in the following parameters: latency, scalability and data protection. Other ethical and governance considerations such as explainability, fairness, alignment with emergent regulatory frameworks are addressed in the review as well. Lastly, the article presents future areas of research such as quantum-resistant cryptographic integration and self-orchestrated FL. EdgeSecFL stands as a robust foundation for developing secure, policy-aware, and scalable intrusion detection systems across modern distributed infrastructures.

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Keywords :

Federated Learning, Intrusion Detection Systems, IoT-Cloud Security, Blockchain, Secure Aggregation, Privacy-Preserving Machine Learning.