Maturing Federated Transfer Learning for Adaptive Beam Selection in mmWave MIMO Systems

Millimeter-wave (mmWave) beam selection in MIMO systems presents significant challenges in dynamic environments due to computational constraints, data heterogeneity, and privacy concerns. In this paper, we propose a novel Maturing Federated Transfer Learning (MFTL) framework that integrates radar and image data to enhance beam prediction accuracy while ensuring user data privacy. The proposed approach utilizes ResNet-50 as a pre-trained model, fine-tuned locally at distributed Antenna Units (AUs) to adapt to diverse scenarios. To mitigate the effects of data heterogeneity, we evaluate multiple aggregation strategies, with FedMedian demonstrating superior robustness compared to FedAvg and weighted averaging. Our optimized MFTL configuration, utilizing a learning rate of 0.005, a batch size of 32, and five aggregation rounds, significantly improves model performance. Experimental results on the DeepSense 6G dataset indicate that our approach achieves a Top-1 accuracy of 57.58% and a Top-5 accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${9 4. 7 \%}$</tex>, outperforming state-of-the-art methods. These findings highlight the effectiveness of federated learning, transfer learning, and robust aggregation techniques in improving beam selection accuracy for next-generation mmWave communication systems.