Abstract : The proposed system shows a federated learning architecture powered by quantum computing that uses deep reinforcement based learning as part of their IoT data security upgrade and subsequent privacy improvement. The framework is based on includes advanced quantum computation to speed up complex tasks while at the same time keeping information handling on a decentralized way among the several types of IoT devices. This research introduces a quantum-assisted federated learning framework integrated with deep reinforcement learning to strengthen data security and privacy in Internet of Things (IoT) environments. The objective is to develop a scalable and adaptive decentralized model that protects sensitive data without relying on centralized repositories. The proposed system combines the distributed intelligence of federated learning, the computational acceleration of quantum processing, and the adaptive decision-making of deep reinforcement learning. This integration allows each IoT node to train locally while quantum-enhanced optimization accelerates convergence and improves model robustness. The deep reinforcement component enables real-time adaptation to dynamic security threats, enhancing system responsiveness.

Keyword : Adaptable framework, decentralized architecture, deep reinforcement, federated learning, IoT data security, privacy preservation, Quantum computing, quantum-improved processing, secure communication, smart IoT systems.