Fedcompass: Federated Clustered and Periodic Aggregation Framework for Hybrid Classical-Quantum Models

2026-02-03

AI Summary

This work addresses the significant performance degradation of hybrid classical-quantum federated learning under non-independent and identically distributed (non-IID) data by proposing a hierarchical aggregation framework. The approach first groups clients via spectral clustering based on the similarity of their class distributions and performs intra-cluster aggregation for classical feature extractors. Concurrently, quantum parameters are aggregated using a cyclic averaging strategy combined with adaptive optimization. This study is the first to integrate spectral clustering with cyclic averaging and introduces distinct aggregation mechanisms tailored separately for classical and quantum parameters, effectively mitigating the adverse effects of data heterogeneity. Experimental results on three benchmark datasets demonstrate that the proposed method improves model accuracy by up to 10.22% and exhibits markedly superior convergence stability compared to six state-of-the-art federated learning baselines.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Federated learning enables collaborative model training across decentralized clients under privacy constraints. Quantum computing offers potential for alleviating computational and communication burdens in federated learning, yet hybrid classical-quantum federated learning remains susceptible to performance degradation under non-IID data. To address this,we propose FEDCOMPASS, a layered aggregation framework for hybrid classical-quantum federated learning. FEDCOMPASS employs spectral clustering to group clients by class distribution similarity and performs cluster-wise aggregation for classical feature extractors. For quantum parameters, it uses circular mean aggregation combined with adaptive optimization to ensure stable global updates. Experiments on three benchmark datasets show that FEDCOMPASS improves test accuracy by up to 10.22% and enhances convergence stability under non-IID settings, outperforming six strong federated learning baselines.

Problem

• federated learning
• quantum computing
• non-IID data
• hybrid classical-quantum models
• performance degradation

Innovation

• federated learning
• hybrid classical-quantum models
• spectral clustering
• circular mean aggregation
• non-IID data