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Jupiter: a modern federated learning platform for regional medical care

Science China(Information Sciences) 2021年第10期64卷 152-165页

作者： Ju XING Jiadong TIAN Zexun JIANG Jiali CHENG Hao YIN Department of Automation Tsinghua University School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science and Technology Tsinghua University College of Engineering Northeastern University

With the emergence of AI technologies, intrinsic value of data is released and takes tremendous effects on numerous industries. In the context of regional medical care, data sharing and cooperating is in high demand, which can bring both financial and societal benefits. At present, however, medical data are locked inside medical facilities owing to legal risks and economic considerations. How to bring AI technologies into full play under this circumstance is a big challenge. In this paper, we propose Jupiter, an easy-to-use,secure, and high-performance platform for federated machine learning. Jupiter constructs a secure and highperformance aggregator cluster with SGX to efficiently aggregate the encrypted model parameters. Jupiter employs a stateful design to cooperate with medical facilities in regional medical systems with a fixed network connection. By providing an innovative programming abstraction, Jupiter makes model development more friendly to developers. The experiments show that with a low memory footprint, the throughput of a single node on an ordinary PC can reach 300 MB/s(with slice size fixed to 64 KB), and the aggregation primitive we built can process 11 k aggregations per second.

关键词： federated learning programming abstraction SGX high-performance

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