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This paper introduces QFedAgent, a hybrid quantum-classical framework designed for personalized federated learning in multi-agent activity recognition. By utilizing a variational quantum circuit fusion module, the method significantly reduces the number of parameters required for modeling sensor interactions, achieving a 10x reduction compared to classical approaches. Experimental results on the OPPORTUNITY dataset show that QFedAgent achieves a mean test accuracy of 97.7%, demonstrating its effectiveness in handling non-IID multimodal sensor streams while maintaining parameter efficiency.
Achieving 97.7% accuracy with a quantum fusion module that slashes parameter counts by 10x could redefine efficiency in federated learning for multi-agent systems.
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.
