Federated Learning for 6G Networks Navigating Privacy Benefits and Challenges

The next generation of wireless communication, 6G, is expected to interconnect billions of ultra‑intelligent devices and deliver sub‑millisecond latency for immersive applications such as holographic telepresence and autonomous mobility. Traditional cloud‑centric AI pipelines cannot scale to this setting because continuous raw‑data transfer overloads backhaul links and exposes sensitive user information. To address this gap, our project implements a federated‑learning (FL) framework in which edge devices train models locally and share only encrypted weight updates with a coordinating server. We prototype the concept in a Flask web application that allows users to register, upload sample beamforming datasets, choose among six candidate algorithms (Random Forest, FNN, CNN, MLP, XGBoost, Decision Tree), and receive real‑time prediction feedback on 6G beam‑forming success. The back‑end simulates an FL round by averaging locally derived model parameters, demonstrating up to 100 % accuracy for tree‑based ensembles while preserving data sovereignty. We also quantify communication savings, latency reductions, and resistance to data‑reconstruction attacks compared with centralized training. Nonetheless, challenges emerge: non‑IID data distributions, straggler devices, model poisoning, and the need for fine‑grained incentive mechanisms. Our findings suggest that integrating differential privacy, secure aggregation, and blockchain‑based audit trails will be crucial for trustworthy, large‑scale FL deployment in 6G ecosystems.

Keywords: 6G, Federated Learning, Edge AI, Privacy‑Preserving Training, Beamforming Prediction, Secure Aggregation