Communication Efficient Adaptive Model-Driven Quantum Federated Learning

TL;DR

This paper introduces a novel quantum federated learning algorithm that significantly reduces communication costs and improves model performance in heterogeneous, large-scale data environments, addressing key scalability and personalization challenges.

Contribution

It presents the first model-driven quantum federated learning approach that enhances efficiency, personalization, and generalization, applicable to various federated learning scenarios.

Findings

50% reduction in communication costs

Maintains or exceeds baseline accuracy

Improves local model training in non-IID settings

Abstract

Training with huge datasets and a large number of participating devices leads to bottlenecks in federated learning (FL). Furthermore, the challenges of heterogeneity between multiple FL clients affect the overall performance of the system. In a quantum federated learning (QFL) context, we address these three main challenges: i) training bottlenecks from massive datasets, ii) the involvement of a substantial number of devices, and iii) non-IID data distributions. We introduce a model-driven quantum federated learning algorithm (mdQFL) to tackle these challenges. Our proposed approach is efficient and adaptable to various factors, including different numbers of devices. To the best of our knowledge, it is the first to explore training and update personalization, as well as test generalization within a QFL setting, which can be applied to other FL scenarios. We evaluated the efficiency of the proposed mdQFL framework through extensive experiments under diverse non-IID data heterogeneity conditions using various datasets within the Qiskit environment. Our results demonstrate a nearly 50% decrease in total communication costs while maintaining or, in some cases, exceeding the accuracy of the final model and consistently improving local model training compared to the standard QFL baseline. Moreover, our experimental evaluation thoroughly explores the QFL and mdQFL algorithms, along with several influencing factors. In addition, we present a theoretical analysis to clarify the complexities of the proposed algorithm. The experimental code is available at 1.