Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things
arXiv:2512.20627v2 Announce Type: replace-cross Abstract: Intent-Based Networking (IBN) offers a promising paradigm for intelligent and automated network control in Industrial Internet of Things (IIoT) environments by translating high-level user intents into executable network strategies. However, frequent strategy deployment and rollback are impractical due to tightly coupled workflows and high downtime costs, while node heterogeneity and privacy constraints further complicate centralized strategy evaluation. To address these challenges, we propose a Federated Evaluation Enhanced Intent-Based
The increasing complexity of IIoT environments and the demand for autonomous, secure network management necessitate advanced AI solutions like federated learning to overcome traditional centralized infrastructure limitations.
This development contributes to more resilient, private, and efficient industrial automation, crucial for critical infrastructure and manufacturing sectors increasingly reliant on connected devices.
The adoption of federated evaluation for IBN allows for more dynamic, private, and scalable network strategy deployment in IIoT, reducing downtime and improving adaptability.
- · Industrial IoT operators
- · AI/ML solution providers
- · Manufacturing sector
- · Federated learning platforms
- · Traditional centralized network management systems
- · Companies with high downtime tolerance
Increased efficiency and security in Industrial Internet of Things (IIoT) network management through federated learning.
Accelerated adoption of AI-driven automation in industrial environments, potentially leading to productivity gains and new service offerings.
Enhanced resilience of critical infrastructure by enabling more robust and self-adapting operational communication networks.
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Read at arXiv cs.AI