Energy-Aware Federated Edge Intelligence for 6G-Enabled Smart Urban Networks

Abstract

The advent of smart urban networks based on 6G computing requires low-latency and intelligent edge computing solutions to support the massive distributed data generation. Federated Learning (FL) is an attractive concept of facilitating privacy-conscious distributed intelligence but the traditional FL models frequently ignore the important limitations like battery capacity, wireless communication energy, and variable participation of scale and heterogeneity in massive urban settings. The energy-aware Federated Edge Intelligence (EA-FEI) framework suggested in this paper aims to optimize the energy consumption of the computation and communication processes, latency, and model quality in 6G smart city scenarios simultaneously. The suggested scheme incorporates the energyconscious client selection, battery-sensitive local training, and channel-conscious compression schemes into a multi-objective optimization scheme. EA-FEI can control unnecessary energy consumption through a dynamic adaptive deployment of participation and communication policies depending on battery level, uplink rate and data drift indicators and ensures a strong convergence. Through experimental assessment, the offered framework is found to offer faster convergence and performs better in classification and reduces the per-round energy consumption by about 2728% relative to traditional FedAvg. The findings support the idea that the introduction of energyawareness into federated learning pipelines is a fundamental requirement towards the realization of scalable and sustainable intelligence in future 6G enabled smart urban networks.