Adaptive Quantum Federated Learning for Autonomous Surveillance Multi-Drone Networks

In this era of automation, drones have considered to be a key element with its wide applicability in various situations. Additionally, the efficiency of drones can be further improved via collaborated learning techniques such as federated learning (FL). However, the drone-based FL requires a significant amount of communications because the drones share their FL data over wireless channels. Therefore, the unstable channel condition of wireless transmission introduces the low success probability in large-scale parameter transmission. According to quantum computing (QC) advantages, this problem can be tackled due to less parameter utilization in quantum neural network (QNN) training. Therefore, a novel QC-based FL known as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive quantum federated learning (AQFL)</i> is proposed for drone license plate recognition in autonomous surveillance applications. In our proposed AQFL, adaptive QNNs (AQNNs) are used as local models which can control the depths of QNNs by adding measurement computations in the middles of QNN architectures. Thus, our proposed AQFL is robust even in training data non-iidness and large local model variances while maintaining sufficient learning performance. Finally, the evaluation results verify that our proposed AQFL achieves the desired performance improvements.