ADDOUN MohammedFERTAS Mouad2026-07-092026-06-22https://dspace.univ-ghardaia.edu.dz/handle/123456789/10630Thesis submitted in partial fulfillment of the requirements for the degree of Master Domain: Mathematics and Computer Science, Field: Computer Science Specialty: Intelligent Systems for Knowledge Extraction FEKAIR Mohamed El Amine/SupervisorThe rapid expansion of the Internet of Things (IoT) has multiplied the attack surface exposed to cyber threats. Traditional intrusion detection systems rely on collecting network data at a central location, which raises serious privacy, bandwidth, and regulatory concerns in IoT environments where devices are heterogeneous, resource-constrained, and geographically distributed. Federated Learning (FL) offers a promising alternative by enabling devices to collaboratively train a shared model without sharing their raw data. This thesis investigates how a federated learning-based framework can be designed to enable distributed IoT devices to collaboratively predict security attacks, while preserving data privacy and handling device heterogeneity. We propose FedShield, a federated learning framework for multi-class IoT attack prediction, built around a lightweight deep learning classifier, Federated Averaging, and a mask-based secure aggregation mechanism. The framework is implemented in Python using TensorFlow and the Flower library, and evaluated on the TON_IoT dataset partitioned across non-IID clients. The experimental results show that the framework reaches strong detection performance on a non-IID partition of the TON_IoT dataset, and that mask-based secure aggregation can be added without measurable utility cost. The work confirms that federated learning is a credible building block for privacy-preserving IoT intrusion detection.enInternet of ThingsIoT SecurityIntrusion Detection SystemFederated LearningPrivacy-Preserving MechanismsTON_IoTThesis