Clustered Federated Learning With Adaptive Pruning for 6G Edge-Intelligent Transportation

The upcoming 6G technology, with its high speed and low latency, is poised to become a foundational technology for intelligent transportation systems. To handle the massive data generated by connected vehicles in 6G environments, federated learning methods are essential. However, traditional centralized federated learning approaches still face challenges related to data and device heterogeneity, which significantly affects training efficiency. To address these challenges, we propose FedCPC, a context-based adaptive pruning clustered federated learning method. Based on the positive correlation between similar data distributions and model representations, we use centralized kernel alignment (CKA) to group clients with similar data distributions, thus reducing the impact of data heterogeneity. Furthermore, we introduce a context-aware random forest multi-armed bandit method to determine appropriate pruning rates based on device capabilities and historical performance which addresses device heterogeneity concerns. Experimental results on open-source datasets demonstrate that FedCPC outperforms traditional FL methods in both learning efficiency and communication effectiveness.