Federated Double Deep Q-Networks for Unbalanced Material Classification in Tactile Internet

Unbalanced material classification with visual–haptic data has become one of the primary concerns of tactile internet, existent classifiers suffer from performance degradation, since: the imbalance across both material classes and visual–haptic samples causes overestimation, some clients experience dropouts in global classifier training, and determining the theoretical convergence of a global classifier is challenging. This article develops the FedDDQN method to cope with the above issues. Specifically, for the server, the optimized federated learning scheme efficiently deals with unbalanced material classes and clients dropouts, and for clients, the adjusted double deep Q-networks (DDQN) effectively avoids the overestimation caused by unbalanced visual–haptic samples and improves representation ability. The convergence of FedDDQN are theoretically examined, using autonomous ordinary differential equation systems and Lyapunov stability. Numerical evaluations show that FedDDQN outperforms existing methods in both classification accuracy and computational complexity.