Neural-network-inspired federated coordination for noise-tolerant distributed optimization in cyber-physical...

Area of Science:

• Artificial Intelligence
• Distributed Systems
• Computational Neuroscience

Background:

• Distributed agent networks require robust coordination mechanisms for complex tasks.
• Cyber-physical systems, like home energy management, face challenges in balancing local autonomy with global optimization.
• Privacy concerns are paramount in data aggregation for coordinated control.

Purpose of the Study:

• To propose and evaluate a neural-network-inspired computational framework for hierarchical, noise-tolerant coordination in distributed agent networks.
• To assess the framework's effectiveness in a cyber-physical demand-side management testbed.
• To ensure privacy-preserving coordination through federated learning techniques.

Main Methods:

• A Privacy-Preserving Federated Congestion-Signal Coordination approach was employed, separating local scheduling from global coordination.
• Home energy management systems utilized genetic algorithms, with a federated coordinator aggregating updates using Gaussian differential privacy.
• Evaluations involved 50 home energy management systems over 30 federated rounds, using real-world demand traces and market prices.

Main Results:

• The proposed framework reduced the peak-to-average ratio by 13.1% (from 1.468 to 1.276).
• Normalized energy cost was within 0.04% of centralized coordination, with minimal variation across privacy budgets.
• The system transmitted significantly fewer numerical values (200x less) than centralized coordination, demonstrating efficient communication.

Conclusions:

• Hierarchical feedback, bounded unit influence, stochastic aggregation, and compressed message passing enable stable, privacy-preserving distributed coordination.
• The framework serves as a testbed for neural-network-inspired principles in collective computation, demonstrating noise tolerance.
• The study highlights the potential for advanced computational frameworks in optimizing distributed systems like smart grids.