Federated Digital Twin Construction via Distributed Sensing: A Game-Theoretic Online Optimization with Overlapping Coalitions

2025-03-21

AI Summary

In edge–cloud collaborative environments, maintaining high-fidelity digital twin (DT) models while minimizing long-term energy consumption and configuration costs remains challenging. Method: This paper proposes a federated DT modeling and optimization framework that decomposes the global DT into local DT components generated by multiple edge servers. It jointly optimizes partial-DT allocation, edge–sensor association, and computational/communication resource scheduling. A hierarchical game-theoretic model is introduced: an upper-layer bilateral matching game ensures stable assignment of DT components to edge servers; a lower-layer overlapping coalition formation game captures dynamic edge collaboration, solved via a switching-rule-based coalition algorithm and a deep reinforcement learning–driven long-term equilibrium solver (DMO). Contribution/Results: Simulation results demonstrate that the framework simultaneously improves DT model accuracy and significantly reduces energy consumption and deployment cost, outperforming state-of-the-art baselines in overall performance.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In this paper, we propose a novel federated framework for constructing the digital twin (DT) model, referring to a living and self-evolving visualization model empowered by artificial intelligence, enabled by distributed sensing under edge-cloud collaboration. In this framework, the DT model to be built at the cloud is regarded as a global one being split into and integrating from multiple functional components, i.e., partial-DTs, created at various edge servers (ESs) using feature data collected by associated sensors. Considering time-varying DT evolutions and heterogeneities among partial-DTs, we formulate an online problem that jointly and dynamically optimizes partial-DT assignments from the cloud to ESs, ES-sensor associations for partial-DT creation, and as well as computation and communication resource allocations for global-DT integration. The problem aims to maximize the constructed DT's model quality while minimizing all induced costs, including energy consumption and configuration costs, in long runs. To this end, we first transform the original problem into an equivalent hierarchical game with an upper-layer two-sided matching game and a lower-layer overlapping coalition formation game. After analyzing these games in detail, we apply the Gale-Shapley algorithm and particularly develop a switch rules-based overlapping coalition formation algorithm to obtain short-term equilibria of upper-layer and lower-layer subgames, respectively. Then, we design a deep reinforcement learning-based solution, called DMO, to extend the result into a long-term equilibrium of the hierarchical game, thereby producing the solution to the original problem. Simulations show the effectiveness of the introduced framework, and demonstrate the superiority of the proposed solution over counterparts.

Problem

• Optimize federated digital twin construction via distributed sensing
• Jointly manage partial-DT assignments and resource allocations
• Maximize DT model quality while minimizing operational costs

Innovation

• Federated framework for digital twin construction
• Game-theoretic online optimization approach
• Deep reinforcement learning-based solution DMO