Performance Analysis and Design of a Weighted Personalized Quantum Federated Learning

Abstract

Advances in federated and quantum computing have improved data privacy and efficiency in distributed systems. Quantum federated learning (QFL), like its classical counterpart, classic federated learning (CFL), struggles with challenges in heterogeneous environments. To address these, we propose wp-QFL, a weighted personalized approach with quantum federated averaging (qFedAvg), tackling non-IID data and local model drift. While CFL personalization has been well explored, its application to QFL remains underdeveloped due to inherent differences. The proposed wp-QFL fills this gap by adapting to data heterogeneity with weighted personalization and drift correction. The code implementation is available at https://github.com/s222416822/wpQFL.