Federated Digital Twins in Real-World Systems: State of the Art, Emerging Challenges, and Systemic Architecture

Federated Digital Twins in Real-World Systems: State of the Art, Emerging Challenges, and Systemic Architecture

Towards a scalable and adaptive infrastructure for modeling complex territorial systems

Author: Sebastiano Martignano, research strategy advisor -CRF Italy

Abstract

Digital Twins (DTs) are rapidly gaining recognition as a transformative digital paradigm—not merely as tools for visualization or simulation, but as dynamic systems capable of evolving with the physical entities they represent. Unlike traditional simulators, which operate within well-defined parameters and use static or predefined models, Digital Twins aim to accompany the physical system throughout its entire lifecycle, adapting to its changing states, behaviors, and environmental interactions. This paper explores what makes a Digital Twin distinct, how its capabilities differ from conventional simulators, and why its full realization requires strategic research investment—particularly in the direction of adaptive and evolutive modeling.

Introduction

In recent years, the concept of the Digital Twin (DT) has evolved from static digital replicas of individual assets into dynamic, data-driven systems capable of modeling, monitoring, and simulating real-world infrastructure. While the adoption of DTs in manufacturing and industry is now relatively mature, the application of Digital Twin architectures to distributed, open, and multi-domain environments—such as territorial systems, circular economy networks, or cross-border infrastructures—remains at an early and exploratory stage.

A growing number of initiatives now propose to build federated networks of Digital Twins, where each node corresponds to a real-world system (e.g., greenhouse, water plant, PV field, port terminal) and contributes to a broader mesh of collaborative modeling and simulation. This shift—from centralized platforms to distributed federations—raises new opportunities but also profound challenges.

This article outlines the current state of the art, identifies the critical bottlenecks in federated DT systems, and introduces a systemic architectural framework grounded in hybrid modeling, semantic interoperability, and multi-agent coordination.

The Promise of Federated Digital Twins

Federated Digital Twins offer a compelling alternative to monolithic or platform-based digitalization. Instead of creating a single, integrated system that attempts to manage all variables, the federated approach builds distributed digital models, each reflecting a real-world system embedded in a specific operational context.

Each node in such a federation:

• captures its own data streams from local sensors and controllers,
• runs simulations based on localized models,
• exposes interfaces or outputs to other nodes in the network.

Federation allows these DTs to interact, exchange, and synchronize, enabling collective simulation of complex phenomena—e.g., resource flows across industrial symbiosis, cascading failures across infrastructure layers, or coordinated scenarios for circular economy transitions.

However, implementing this vision at scale and in real-world conditions exposes four core challenges, examined below.

Hybrid Modeling: The Core of Real-World Digital Twins

One of the primary limitations of current DT implementations lies in the fragility and rigidity of the models themselves. While traditional Digital Twins often rely on first-principles models (e.g., thermodynamics, fluid dynamics, structural mechanics), these become difficult to maintain and scale in dynamic, data-rich, and uncertain environments.

Purely data-driven models, on the other hand—such as neural networks or black-box regressors—lack explainability, robustness, and generalizability outside of narrow contexts.

Hybrid models, which combine the structure of physical models with the flexibility of data-driven components, offer a middle ground.

These models:

• preserve causal structures and conservation laws,
• incorporate machine learning components to capture unmodeled dynamics,
• adapt over time as new data becomes available.

In a federated DT system, hybrid modeling is essential because:

• each node must operate autonomously, using models that reflect its unique configuration;
• yet it must also participate in shared simulations, requiring models that are comparable and composable.

Challenge: Despite recent research progress (e.g., physics-informed machine learning, symbolic regression, surrogate modeling), there is still no unified methodology for constructing, validating, and exchanging hybrid models in federated environments.

Interoperability and the Problem of Semantics

The second critical barrier lies in semantic interoperability. Unlike closed systems, a federation of Digital Twins must allow:

• different systems to share data and models in ways that are understandable and meaningful,
• variable names, units, structures, and assumptions to be interpreted and translated dynamically.

While syntactic interoperability (e.g., APIs, data formats) is relatively well managed, semantic interoperability remains fragile and largely manual.

Existing efforts (e.g., SAREF, QUDT, AGROVOC) provide useful ontological frameworks, but:

• are often domain-specific, incomplete, and non-extensible,
• lack contextual adaptability, especially in real-time environments,
• do not support automated mapping between divergent local schemas.

Challenge: Federated DTs require a new class of semantic infrastructures, capable of:

• expressing variable-level metadata,
• adapting to evolving vocabularies and use cases,
• enabling on-the-fly negotiation of meaning across heterogeneous nodes.

Federation as a Scalable Multi-Agent System

A federation of DTs can be conceptualized as a multi-agent system (MAS), in which:

• each DT acts as an autonomous agent,
• agents interact via shared scenarios, messages, and feedback loops,
• the system exhibits emergent coordination without centralized control.

However, building MAS on top of real-world infrastructure is non-trivial. Key research issues include:

• Scalability: how to maintain performance and responsiveness as nodes are added or removed?
• Adaptivity: how to allow nodes to learn, adjust, or negotiate behavior over time?
• Conflict management: how to resolve conflicting objectives or inconsistent assumptions between agents?

To address these issues, we propose a taxonomy of MAS properties relevant to federated DT networks:

Challenge: Implementing this taxonomy requires new mechanisms for discovery, coordination, and governance in digital ecosystems where no single authority controls the whole.

Toward an Operational Research Infrastructure

The transition to a real-world federated DT network implies a rethinking of research itself. Rather than developing models in silico and testing them on static datasets, researchers can:

• deploy new modeling approaches directly in live nodes,
• run comparative experiments across domains and regions,
• observe learning dynamics in real-time,
• use the network as a testbed for resilience, interoperability, and governance.

Such a system can host:

• live challenges (e.g., drought scenario simulation, port congestion optimization),
• collaborative experiments between academic, public, and private actors,
• open competitions for model improvement or semantic translation.

In this vision, the federated DT network becomes not just a tool for managing reality, but a platform for advancing scientific understanding of how complex systems adapt and cooperate.

Conclusion

Federated Digital Twins represent a major shift in how we model, understand, and govern complex systems. But their realization requires:

• new hybrid modeling paradigms,
• new semantic infrastructures,
• and new approaches to distributed intelligence.

These are not solved problems—they are research frontiers. And only by grounding them in real, interconnected, operational nodes can we move from theory to transformation.

The next phase is not simply to deploy more DTs, but to construct the conditions for their federation—and to let intelligence emerge, not from design, but from interaction.

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