Technology Convergence Report 2025

Distributed edge intelligence networks Distributed edge intelligence networks fundamentally reimagine the computing landscape by embedding AI capabilities directly at the network periphery where data originates, creating an intelligent mesh of computational nodes that process information locally rather than relying on centralized facilities. This combination enables organizations to extract value from their internet of things (IoT) devices, sensors and other data-generating technologies without the infrastructure costs and vulnerabilities associated with centralized processing.This combination is particularly evident in how federated learning transforms the fundamental approach to AI model development. Rather than centralizing sensitive data for training, federated learning distributes the training process across edge devices, allowing models to learn from local data without ever transferring it to centralized servers. Only the insights (model updates) travel across the network, dramatically reducing data exposure while still enabling collective intelligence improvement. When combined with specialized edge AI algorithms and hardware, these systems achieve both privacy protection and computational efficiency that would be impossible with any singular technology. 3 2 3 2 2 2Distributed edge intelligent systemsSmall/efficient models Edge AI Mobile edge computing Edge analyticsIndustrial loTFederated learning Technology domain Artificial intelligence Omni compute Maturity level 3 Product4 Commodity 1 Genesis2 Custom-builtN Industry Company Outcome Energy and utilitiesVestasIntelligent processing optimizes wind energy infrastructure by improving turbine efficiency, enabling predictive maintenance and maximizing renewable generation. Telecommunications Zetic.aiDistributed processing in remote, bandwidth-constrained environments delivers improved safety, responsiveness and operational efficiency. As organizations increasingly deploy IoT devices, sensors and other data-generating technologies at the network edge, this combination pattern enables them to extract value from that data without the infrastructure costs and vulnerabilities associated with centralized processing. Technology Convergence Report 17