Privacy- preserving network slice classification in 6G using federated CNN-BiLSTM

This paper presents a federated learning (FL) framework that integrates a lightweight Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network for privacy-preserving 6 G network slice classification. Using the simulated 6 G Network Slicing Dataset, the model learns from distributed edge clients without accessing raw user data, where the CNN extracts spatial QoS features and the BiLSTM models temporal dependencies. Federated training is performed using FedAvg, aggregating locally trained client models at the orchestrator. The proposed federated CNN–BiLSTM architecture achieves strong experimental performance- including 97.8% cost efficiency, 97.9% energy efficiency, 1.3 ms jitter, 0.22 s latency, 98.6% QoS score, 0.045 BER, 1.6% packet drop rate, and 960 Mbps throughput, outperforming baselines such as FDRL-RAN, DRL-DDQT, and centralized CNN–BiLSTM. Although evaluated on synthetic data, the model shows high potential for real-world 6 G deployment. Future work includes integrating differential privacy and secure aggregation. Unlike prior FL methods that rely on reinforcement learning or heavy optimization-based slice controllers, the proposed design leverages a compact 145k-parameter spatio-temporal model ($$<8$$ MB) tailored for low-power 6 G edge devices, offering an efficient and privacy-aware solution for next-generation slice classification.