Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention

Chen, Ju; Zhao, Ruihui; Sun, Jing; Wei, Xiguang; Zhao, Bo; Liu, Yang; Li, Hongshan; Chen, Tianjian; Zhang, Xinwei; Gao, David Yang; Tan, Ben; Yu, Han; He, Chuning; Yuan, Jiao

doi:10.48550/arxiv.2006.10517

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…We hope that researchers in diverse FL applications can contribute more valuable models and realistic datasets to our community. Promising application domains include, but are not limited to, computer vision [77,78], natural language processing [79,80,81,82,83], finance [74,35,84], transportation [85,86,87,88,89,90,91,92,93,94], digital health [95,96,97,98,99,100,101], recommendation [102,103,104,105,106,107], robotics [108,109], and smart cities [110,111].…”

Section: Architecture Designmentioning

confidence: 99%

FedML: A Research Library and Benchmark for Federated Machine Learning

He,

Li,

et al. 2020

Preprint

Self Cite

116

159

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Federated learning is a rapidly growing research field in the machine learning domain. Although considerable research efforts have been made, existing libraries cannot adequately support diverse algorithmic development (e.g., diverse topology and flexible message exchange), and inconsistent dataset and model usage in experiments make fair comparisons difficult. In this work, we introduce FedML, an open research library and benchmark that facilitates the development of new federated learning algorithms and fair performance comparisons. FedML supports three computing paradigms (distributed training, mobile on-device training, and standalone simulation) for users to conduct experiments in different system environments. FedML also promotes diverse algorithmic research with flexible and generic API design and reference baseline implementations. A curated and comprehensive benchmark dataset for the non-I.I.D setting aims at making a fair comparison. We believe FedML can provide an efficient and reproducible means of developing and evaluating algorithms for the federated learning research community. We maintain the source code, documents, and user community at https://FedML.ai.

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Section: Architecture Designmentioning

confidence: 99%

FedML: A Research Library and Benchmark for Federated Machine Learning

He,

Li,

et al. 2020

Preprint

Self Cite

116

159

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“…In healthcare, some work focuses on a specific disease to design transfer methods, such as blood pressure [18], heart disease [13], Covid-19 [22], etc. ; there are also work focusing on privacy issues and designing corresponding algorithms [12].…”

Section: Related Workmentioning

confidence: 99%

Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals

Ding¹,

Rong²,

Han³

et al. 2023

Preprint

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There are significant regional inequities in health resources around the world. It has become one of the most focused topics to improve health services for data-scarce hospitals and promote health equity through knowledge sharing among medical institutions. Because electronic medical records (EMRs) contain sensitive personal information, privacy protection is unavoidable and essential for multi-hospital collaboration. In this paper, for a common disease in ICU patients, sepsis, we propose a novel cross-center collaborative learning framework guided by medical knowledge, SofaNet, to achieve early recognition of this disease. The Sepsis-3 guideline, published in 2016, defines that sepsis can be diagnosed by satisfying both suspicion of infection and Sequential Organ Failure Assessment (SOFA) greater than or equal to 2. Based on this knowledge, SofaNet adopts a multi-channel GRU structure to predict SOFA values of different systems, which can be seen as an auxiliary task to generate better health status representations for sepsis recognition. Moreover, we only achieve feature distribution alignment in the hidden space during cross-center collaborative learning, which ensures secure and compliant knowledge transfer without raw data exchange. Extensive experiments on two open clinical datasets, MIMIC-III and Challenge, demonstrate that SofaNet can benefit early sepsis recognition when hospitals only have limited EMRs. CCS CONCEPTS• Applied computing → Health informatics; • Security and privacy → Privacy protections.

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“…Ref [72] Outlined and predicted stroke risk and implemented the FL model on cloud servers while protecting user privacy. The federated prediction model system asynchronously enables numerous client connections and arbitrary local gradient iterations in each communication round.…”

Section: Data Heterogeneity In Fl Intensive Care Datasetmentioning

confidence: 99%

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Rauniyar¹,

Hagos²,

Jha³

et al. 2022

Preprint

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With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/Deep Learning (DL) algorithms, the data-driven medical application has emerged as a promising tool for designing reliable and scalable diagnostic and prognostic models from medical data. Hence, in recent years, data-driven medical applications have attracted a great deal of attention from academia to industry. This has undoubtedly improved the quality of healthcare delivery. However, AI-based medical applications still have poor adoption due to their difficulties in satisfying strict security, privacy, and quality of service standards such as low latency. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. It also review and discuss the current research trends and their outcomes for designing reliable and scalable FL models. We outline the general FL's statistical problems, device challenges, security, privacy concerns, and its potential in the medical domain. Moreover, our study is also focused on medical applications where we highlight the burden of global cancer and the efficient use of FL for the development of computer-aided diagnosis tools for addressing them. Additionally, the recent literature revealed that FL models ensure a high level of robustness and generalization compared to the traditional data-driven medical applications. We hope that this review serves as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this field.

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Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention

Abstract: Equal Contribution tion of patients' statistics and demonstrates performance comparisons between the federated prediction model and the single prediction model during the federated training process.

Cited by 5 publications

References 12 publications

FedML: A Research Library and Benchmark for Federated Machine Learning

FedML: A Research Library and Benchmark for Federated Machine Learning

Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

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