Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

Federated learning systems enable the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, current federated learning systems face challenges from various issues such as unpractical system assumptions, scalability and efficiency. Inspired by federated systems in other fields such as databases and cloud computing, we investigate the characteristics of federated learning systems. We find that two important features for other federated systems, i.e., heterogeneity and autonomy, are rarely considered in the existing federated learning systems. Moreover, we provide a thorough categorization for federated learning systems according to four different aspects, including data partition, model, privacy level, and communication architecture. Lastly, we take a systematic comparison among the existing federated learning systems and present future research opportunities and directions.