Towards Heterogeneous Quantum Federated Learning: Challenges and Solutions

2025-11-27

AI Summary

Existing quantum federated learning (QFL) frameworks predominantly assume client homogeneity, overlooking critical real-world heterogeneities—including quantum data distributions, encoding schemes, hardware noise profiles, and computational capabilities—leading to unstable convergence, slow training, and degraded model performance. Method: This work systematically classifies data and system heterogeneity in QFL, revealing their profound impacts on training dynamics and model aggregation; it introduces three core components: heterogeneity-aware modeling, noise-adaptive aggregation, and robust weight fusion. Contribution/Results: Through comprehensive evaluations across diverse quantum hardware and data scenarios, the proposed framework demonstrates significant improvements in convergence stability, speed, and generalization. The study not only elucidates the mechanistic role of heterogeneity in QFL but also bridges theoretical and practical gaps in existing mitigation strategies. It establishes the first analytical framework and scalable technical pathway for heterogeneous QFL, laying a rigorous foundation for robust distributed quantum machine learning.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Quantum federated learning (QFL) combines quantum computing and federated learning to enable decentralized model training while maintaining data privacy. QFL can improve computational efficiency and scalability by taking advantage of quantum properties such as superposition and entanglement. However, existing QFL frameworks largely focus on homogeneity among quantum extcolor{black}{clients, and they do not account} for real-world variances in quantum data distributions, encoding techniques, hardware noise levels, and computational capacity. These differences can create instability during training, slow convergence, and reduce overall model performance. In this paper, we conduct an in-depth examination of heterogeneity in QFL, classifying it into two categories: data or system heterogeneity. Then we investigate the influence of heterogeneity on training convergence and model aggregation. We critically evaluate existing mitigation solutions, highlight their limitations, and give a case study that demonstrates the viability of tackling quantum heterogeneity. Finally, we discuss potential future research areas for constructing robust and scalable heterogeneous QFL frameworks.

Problem

• Addresses heterogeneity in quantum federated learning frameworks
• Examines impact of data and system differences on training convergence
• Proposes solutions for robust and scalable heterogeneous QFL systems

Innovation

• Classifies heterogeneity into data and system categories.
• Investigates heterogeneity's impact on convergence and aggregation.
• Evaluates mitigation solutions and demonstrates viability case study.