QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
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Computer Science > Machine Learning
Title:QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.02426 [cs.LG] |
| (or arXiv:2607.02426v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.02426
arXiv-issued DOI via DataCite (pending registration)
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