这篇论文用72个量子参数做融合，比经典方法少30倍，准确率还97.7%，适合关注联邦学习参数效率的人。
QFedAgent是一种混合量子-经典个性化联邦学习框架，专为多智能体活动识别设计。其变分量子电路融合模块仅需72个量子旋转参数，而经典多层感知机融合需33K参数，实现约10倍总参数减少。在OPPORTUNITY数据集上，基于主体的非独立同分布划分下，该框架达到97.7%的平均测试准确率。实验证明，参数高效的量子融合性能与常规联邦基线相当。
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, making it suitable for privacy-sensitive robotic sensing applications. However, multi-agent systems generate heterogeneous and non-independent and identically distributed (non-IID) multimodal sensor streams that degrade conventional FL algorithms, while classical fusion modules introduce substantial parameter overhead and communication cost. This paper proposes QFedAgent, a hybrid quantum-classical personalized FL framework for multi-agent activity recognition. The approach integrates a variational quantum circuit fusion module that models accelerometer--gyroscope interactions through quantum state encoding and entanglement, requiring only 72 quantum rotation parameters versus 33K in classical multi-layer perceptron-based fusion, achieving approximately 10x total parameter reduction. Experiments on the OPPORTUNITY dataset under subject-based non-IID partitions demonstrate 97.7% mean test accuracy, confirming that parameter-efficient quantum fusion remains competitive with conventional federated baselines.