Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of data.It is challenging and infeasible to transfe...Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of data.It is challenging and infeasible to transfer and process trillions and zillions of bytes using the current cloud-device architecture.展开更多
As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploi...As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploiting its potential because the data usually sits in data silos,and privacy and security regulations restrict their access and use.To address these issues,we built a secured and explainable machine learning framework,called explainable federated XGBoost(EXPERTS),which can share valuable information among different medical institutions to improve the learning results without sharing the patients’ data.It also reveals how the machine makes a decision through eigenvalues to offer a more insightful answer to medical professionals.To study the performance,we evaluate our approach by real-world datasets,and our approach outperforms the benchmark algorithms under both federated learning and non-federated learning frameworks.展开更多
文摘Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of data.It is challenging and infeasible to transfer and process trillions and zillions of bytes using the current cloud-device architecture.
文摘As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploiting its potential because the data usually sits in data silos,and privacy and security regulations restrict their access and use.To address these issues,we built a secured and explainable machine learning framework,called explainable federated XGBoost(EXPERTS),which can share valuable information among different medical institutions to improve the learning results without sharing the patients’ data.It also reveals how the machine makes a decision through eigenvalues to offer a more insightful answer to medical professionals.To study the performance,we evaluate our approach by real-world datasets,and our approach outperforms the benchmark algorithms under both federated learning and non-federated learning frameworks.
