Quantum-enhanced Federated Learning Framework for Large-scale Traffic Anomaly Detection

Abstract

The growing pace of development in smart transportation systems has raised new benchmarks for traffic information, introducing the need to ensure safety, efficiency, and security by devising complex anomaly detection systems. Privacy concerns, scale limitations, and computational constraints are among the significant limitations associated with the classical centralised approaches to machine learning. The suggested paper presents a novel theoretical framework that may be applied to integrate the ideas of quantum computing and federated learning systems to address the issue of detecting anomalies in the traffic data at large scales. The suggested quantum-enhanced federated learning (QEFL) system can attain geometric increases in the rate of pattern recognition, and the system does not violate the privacy of the data due to the decentralized learning and capabilities of quantum superposition and entanglement. Examine the theory behind quantum machine learning algorithms, examine federated learning protocols in traffic networks and propose a hybrid design to prepare quantum variational circuits using classical federated aggregation protocols. The model handles such critical questions as the reduction of the quantum noise, communication and heterogeneous data distribution among the traffic nodes. Theoretical analysis demonstrates that quantum advantage would be realized to make the detection of undetectable anomalies, such as cyberattacks, unusual traffic patterns, and infrastructure failures, simultaneously without violating the regulations of securing the data.