A Survey of Federated Learning for mmWave Massive MIMO

Millimeter wave massive multi input multi output (mmWave m-MIMO) holds immense potential for beyond 5G networks, however its practical implementation faces hurdles in beamforming, channel estimation, and signal detection. Deep learning (DL) has emerged as a promising solution for tackling these challenges, but centralized learning (CL) approaches raise concerns about privacy and communication overhead. This is where federated learning (FL) steps in as a compelling alternative. By distributing the workload to user devices while respecting data privacy, FL offers a promising avenue for overcoming the limitations of CL in mmWave m-MIMO applications. This paper delves into the current and future advancements of FL in the context of mmWave m-MIMO. We specifically focus on how FL can be applied to channel estimation, beamforming, and signal detection within this framework. Furthermore, we summarize relevant open source dataset, code, and FL framework to facilitate the development of FL for tackling challenges in mmWave m-MIMO. To conclude, we analyze the key research challenges and opportunities that lie ahead for the development of FL in mmWave m-MIMO applications.