Quantum circuits can replace classical fusion layers in federated learning with 72 parameters instead of 33K, making multi-agent activity recognition more practical for resource-constrained robotic systems.
This paper presents QFedAgent, a federated learning system for activity recognition across multiple robotic agents. It uses quantum circuits to fuse sensor data (accelerometer and gyroscope) more efficiently than classical neural networks, reducing parameters by 10x while maintaining accuracy on distributed, non-uniform data.