Adaptive Federated Learning to Optimize Integrated Flows in Cyber-Physical Data Centers

TL;DR

This paper introduces an adaptive federated learning approach to optimize integrated energy, heat, and data flows in distributed data centers, enhancing efficiency while preserving privacy and data integrity.

Contribution

It proposes a novel federated learning-to-optimization framework with cryptography and convergence guarantees for privacy-preserving, efficient large-scale data center management.

Findings

Achieves near-optimal performance in data center flow optimization.

Ensures privacy and data integrity through cryptography and double aggregation.

Demonstrates high computational efficiency in large-scale simulations.

Abstract

Data centers play an increasingly critical role in societal digitalization, yet their rapidly growing energy demand poses significant challenges for sustainable operation. To enhance the energy efficiency of geographically distributed data centers, this paper formulates a multi-period optimization model that captures the interdependence of electricity, heat, and data flows. The optimization of such integrated multi-domain flows inherently involves mixed-integer formulations and the access to proprietary or sensitive datasets, which correspondingly exacerbate computational complexity and raise data-privacy concerns. To address these challenges, an adaptive federated learning-to-optimization approach is proposed, accounting for the heterogeneity of datasets across distributed data centers. To safeguard privacy, cryptography techniques are leveraged in both the learning and optimization processes. A model acceptance criterion with convergence guarantee is developed to improve learning performance and filter out potentially contaminated data, while a verifiable double aggregation mechanism is further proposed to simultaneously ensure privacy and integrity of shared data during optimization. Theoretical analysis and numerical simulations demonstrate that the proposed approach preserves the privacy and integrity of shared data, achieves near-optimal performance, and exhibits high computational efficiency, making it suitable for large-scale data center optimization under privacy constraints.