Neural-network-inspired federated coordination for noise-tolerant distributed optimization in cyber-physical demand-side management
Introduction This study proposes a neural-network-inspired computational framework for hierarchical, noise-tolerant coordination in distributed agent networks. The framework is evaluated in a cyber-physical demand-side management testbed in which autonomous home energy management systems perform local scheduling under a shared global price signal. Methods Privacy-Preserving Federated Congestion-Signal Coordination separates local appliance scheduling from global adaptive coordination. Each home energy management system uses a genetic algorithm with a population of 100 over 50 generations. A federated coordinator aggregates clipped congestion-gradient updates and applies Gaussian differential privacy with a noise multiplier of 1.1 and a clipping norm of 1.0. The main evaluation was conducted over 30 federated rounds using 50 home energy management systems, EirGrid demand traces, SEM-O market prices, and 10 independent simulation runs. A complementary noise-location analysis used 20 home energy management systems and five independent seeds. Results The proposed method reduced the peak-to-average ratio from 1.468 to 1.276, corresponding to a statistically significant 13.1% improvement. Its normalized energy cost remained within 0.04% of centralized coordination. Energy cost varied by only 0.016% across eight evaluated privacy budgets, and the framework transmitted 200 times fewer numerical values than centralized coordination at 50 agents. In the complementary noise-location experiment, aggregation-stage noise was better tolerated than local-encoding noise at four of eight evaluated noise levels. Discussion The results show that hierarchical feedback, bounded unit influence, stochastic aggregation, and compressed message passing can support stable privacy-preserving distributed coordination. The framework is not a biological neural-circuit model but provides a reproducible testbed for studying neural-network-inspired principles of noise-tolerant collective computation.