QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition

• URL: http://arxiv.org/abs/2607.02426v1
• Date: Thu, 02 Jul 2026 16:54:35 GMT
• Title: QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
• Abstract summary: QFedAgent is a hybrid quantum-classical personalized FL framework for multi-agent activity recognition.<n>The approach integrates a variational quantum circuit fusion module that models accelerometer-gyroscope interactions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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