Federated Learning for Vision-Based Applications in 6G Networks: A Simulation-Based Performance Study

Manuel J. C. S. Reis^1,*, Nishu Gupta²
1 Engineering Department & IEETA, University of Trás-os-Montes e Alto Douro, Quinta de Prados, Vila Real, 5000-801, Portugal
2 IEETA, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810-193, Portugal
* Corresponding Author: Manuel J. C. S. Reis. Email: email
(This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.073366

Received 16 September 2025; Accepted 07 November 2025; Published online 02 December 2025

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Abstract

The forthcoming sixth generation (6G) of mobile communication networks is envisioned to be AI-native, supporting intelligent services and pervasive computing at unprecedented scale. Among the key paradigms enabling this vision, Federated Learning (FL) has gained prominence as a distributed machine learning framework that allows multiple devices to collaboratively train models without sharing raw data, thereby preserving privacy and reducing the need for centralized storage. This capability is particularly attractive for vision-based applications, where image and video data are both sensitive and bandwidth-intensive. However, the integration of FL with 6G networks presents unique challenges, including communication bottlenecks, device heterogeneity, and trade-offs between model accuracy, latency, and energy consumption. In this paper, we developed a simulation-based framework to investigate the performance of FL in representative vision tasks under 6G-like environments. We formalize the system model, incorporating both the federated averaging (FedAvg) training process and a simplified communication cost model that captures bandwidth constraints, packet loss, and variable latency across edge devices. Using standard image datasets (e.g., MNIST, CIFAR-10) as benchmarks, we analyze how factors such as the number of participating clients, degree of data heterogeneity, and communication frequency influence convergence speed and model accuracy. Additionally, we evaluate the effectiveness of lightweight communication-efficient strategies, including local update tuning and gradient compression, in mitigating network overhead. The experimental results reveal several key insights: (i) communication limitations can significantly degrade FL convergence in vision tasks if not properly addressed; (ii) judicious tuning of local training epochs and client participation levels enables notable improvements in both efficiency and accuracy; and (iii) communication-efficient FL strategies provide a promising pathway to balance performance with the stringent latency and reliability requirements expected in 6G. These findings highlight the synergistic role of AI and next-generation networks in enabling privacy-preserving, real-time vision applications, and they provide concrete design guidelines for researchers and practitioners working at the intersection of FL and 6G.