Robust Stochastic Power Allocation for Industrial IoT Federated Learning with Neurosymbolic AI

In this work, a robust optimization approach for energy-aware federated learning (FL) in industrial IoT networks is proposed that addresses uncertainties in harvested energy, device failures, and dynamic topologies. The proposed neurosymbolic reasoning approach combines graph neural networks (GNNs) for topology-aware power prediction with symbolic rules to solve the stochastic power allocation problem, providing both optimality guarantees and explainable safety-critical decisions. The hierarchical Master-Coordination-Task Agent (MA-CoA-TA) architecture prioritizes critical industrial nodes while ensuring FL convergence under energy constraints. This work establishes approximation guarantees through theoretical analysis relative to the robust optimum and validates with rigorous simulations against existing methods. Experimental results demonstrate that proposed framework provides optimal balance for robust FL deployment in large-scale IIoT networks with real-world uncertainties by achieving 5.7% FL accuracy with 151 J remaining battery under the most challenging conditions (100 rounds, 200 devices), while baselines fail completely (0% accuracy, battery depletion). Ablation confirms component synergy—symbolic reasoning delivers 2.2 times accuracy over GNN-only, while GNN+harvesting preserves 30 times more battery than symbolic-only.