用量子电路融合传感器数据，让多机器人协作识别动作更高效
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
- 用量子电路编码加速度计与陀螺仪数据，替代传统神经网络融合
- 参数量减少10倍，测试准确率达97.7%
- 适合资源受限的多智能体隐私保护场景
联邦学习（FL）可在不共享原始数据的情况下实现分布式设备协同训练，适用于隐私敏感的机器人感知应用。然而，多智能体系统产生的异构且非独立同分布（non-IID）的多模态传感器流会降低传统联邦学习算法性能，而经典融合模块带来显著的参数开销和通信成本。本文提出QFedAgent，一种用于多智能体活动识别的混合量子-经典个性化联邦学习框架。该方法集成可变量子电路融合模块，通过量子态编码与纠缠建模加速度计-陀螺仪交互关系，仅需72个量子旋转参数，相比基于多层感知机的融合模块（33K参数）实现约10倍总参数减少。在基于用户划分的OPPORTUNITY数据集上实验表明，平均测试准确率达到97.7%，证实参数高效的量子融合在性能上仍可媲美传统联邦基线。
原文摘要 · Abstract (English)
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.
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