<![CDATA[Rapid development of sixth-generation (6G) wireless networks demands intelligent communication technologies capable of simultaneously achieving ultra-reliable low-latency communications (URLLC), high spectral efficiency, massive connectivity, and privacy-preserving distributed intelligence. Conventional centralized beamforming optimization methods for Massive Multiple-Input Multiple-Output (Massive MIMO) systems often experience excessive communication overhead, scalability limitations, and privacy concerns, reducing their effectiveness in highly dynamic wireless environments. This study aimed to develop and evaluate a Federated Learning-based Massive MIMO beamforming optimization framework that enhances communication reliability, minimizes latency, and improves resource utilization without exchanging raw user data. Quantitative computational research integrating mathematical modeling, distributed machine learning, wireless network simulation, and statistical performance evaluation was conducted using MATLAB, Python, TensorFlow Federated, and NS-3 under heterogeneous 6G communication scenarios. Performance indicators included beamforming accuracy, packet delivery reliability, end-to-end latency, spectral efficiency, signal-to-interference-plus-noise ratio, convergence speed, communication overhead, and energy efficiency. Experimental results demonstrated beamforming accuracy of 98.43%, packet delivery reliability of 99.998%, average latency of 0.74 ms, significant improvements in spectral and energy efficiency, accelerated model convergence, and substantially reduced communication overhead compared with centralized optimization approaches. Findings confirm that integrating Federated Learning with Massive MIMO beamforming provides a scalable, privacy-preserving, and highly efficient optimization framework capable of supporting future AI-native 6G networks and mission-critical wireless communication services.]]>
