Heterogeneous Federated Learning for Building Intelligent Operating Methods

Designing intelligent operating methods is a key for constructing autonomous operating abilities of core devices such as spacecraft. Benefiting from the development of machine learning techniques, current intelligent operating methods driven by data have shown significant improvements on the ability of autonomous operation. However, viewing the trend of spacecraft clusters, traditional methods are challenged by two key requirements, distributed learning and privacy protection. A feasible solution is based on federated learning whose major concerns are how to learn efficiently in a distributed way with privacy performance guarantee. Core devices like spacecraft usually work in extreme environments and are very limited on the resources of computation and communication, and different devices show significant heterogeneous characteristics on data distributions, computation resources and so on. The heterogeneous characteristics can reduce the performance of general federated learning methods. Therefore, in this paper, based on the idea of grouping models, a federated learning algorithm for constructing intelligent operation methods is proposed, which is designed with consideration of the heterogeneous characteristics. The proposed method can reduce the waiting costs among different heterogeneous devices, adjust the timing of local learning of different devices, provide different models for devices with significantly different data distributions, and achieve the goal of improving the performance of operation models obtained by federated learning. Experimental results are conducted to show the the effectiveness of the proposed method