As clean energy, wind power plays an increasingly important role in improving China’s energy structure. Data on wind farm units and equipment may contain relevant privacy-sensitive information. Once the information is divulged, it will bring huge economic and legal risks to the wind farm. Federated learning (FL) is an important privacy-preserving computing technique, through which model training and inference are completed without transmitting raw data, so as to achieve joint computation among all participants without privacy disclosure and effectively deal with challenges in analyzing wind power data. However, significant communication overheads generated during FL computation have become a major performance bottleneck that has limited the application of the FL technique in wind power scenarios. Therefore, this study takes the typical FL algorithm, namely, XGBoost, as an example and deeply analyzes the communication problems in FL computation. In addition, the study proposes a solution that RDMA shall be utilized as the underlying transport protocol and designs a set of high-performance FL platform communication libraries, which effectively improves the performance of the FL system.