AI-Driven 5G Networks: Federated Optimization for Sustainable Telecommunications

Abstract

A novel approach to making telecommunications infrastructure less damaging to the environment and more energy efficient is to integrate AI with 5G networks. However, the main issues with current approaches are scalability, data security, and a thorough assessment of sustainability. In order to overcome these constraints, this study creates and evaluates a unique AI-driven optimization system that combines blockchain-secured digital twins, federated learning (FL), and ISO-compliant life cycle assessment (LCA). With empirical validation across many operator datasets, the paradigm shows significant gains in network sustainability via thorough mathematical modeling of DQN and LSTM topologies. The main conclusions show that, although data privacy is maintained, PySyft-based FL implementations reduced operational carbon emissions by 30.4% and base station energy consumption by 32.7%. The most significant contributions include (1) a blockchain-CoTwin architecture that enables safe coordination between multiple operators with a discernible computational overhead of 15%–20%, (2) a novel combination of telecommunications performance data and environmental metrics from ReCiPe 2016 that demonstrates both operational benefits and hitherto unmeasured embodied training effects, and (3) empirically validated implementation thresholds that link scholarly research with practical applications. Key infrastructural connection is 9.7 percentage points better in urban installations than in rural ones. According to stakeholder validation, for adoption to take place, interfaces and conventions need to make sense. This research provides a scalable approach to network improvement that integrates cutting-edge technology while respecting legal obligations and protecting the environment. It also lays out new protocols for the creation of 6G networks and the integration of AI into 5G networks.

Received: 19 February 2025 | Revised: 20 October 2025 | Accepted: 21 November 2025

Conflicts of Interest

The author declares that he has no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study

Author Contribution Statement

Gabriel Silva Atencio: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.