An effective quantum federated learning approach for dynamic classification using realistic case studies and IBMQ implementation

Abstract

Currently, data privacy and security are critical in deep learning, especially with biometric data. Federated learning (FL) addresses these concerns, but struggles with security, complexity, and high-dimensional data. Quantum Federated Learning (QFL) overcomes these issues by combining FL with quantum computing, thereby offering greater power and parallelism. In this paper, we propose an innovative QFL approach with a quantum-classical hybrid architecture to address the limitations of classical FL and achieve superior performance in video and image classification. The proposed architecture consists of a classical server and quantum-classical clients. On the client side, after preprocessing the videos and images, a pre-trained model extracts high-level features, which are then classified by a client-specific quantum circuit using a Variational Quantum Circuit (VQC). To maximize the performance of this hybrid system, we jointly optimize the VQC parameters and the weights of the pre-trained model's final feature-extraction layers. The proposed approach has been extensively evaluated in a simulation environment with random client selection to account for the heterogeneity of real-world systems. Furthermore, we present results obtained from quantum computers using globally optimized VQC parameters. This successful validation on actual quantum hardware underscores the practical applicability of our approach, extending its significance beyond theoretical contributions to next-generation applications such as smart campus security.