Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data

Boughorbel, Sabri; Jarray, Fethi; Venugopal, Neethu; Moosa, Shabir; Elhadi, Haithum; Makhlouf, Michel

doi:10.48550/arxiv.1910.12191

Cited by 10 publications

(14 citation statements)

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“…For more details of hyperparameter setting, V-FedMV uses 𝛽 𝑘 as 4, 𝜁 𝑘 and 𝜂 from 2 0 to 2 5 . In H-FedMV, we set the max communication rounds as 20, and the value of 𝛽 𝑙 𝑘 as 4, and 𝜁 𝑙 𝑘 , 𝜂 𝑙 across different clients to be the same from 2 0 to 2 5 . For both V-FedMV and H-FedMV, the optimal parameters are also selected by validation.…”

Section: Methodsmentioning

confidence: 99%

“…For instance, a large 𝜁 𝑘 indicates a higher impact of the 𝑘-th view of model training. In this experimental setting, we test each hyperparameter ∈ {2 0 , 2 1 , 2 2 , 2 3 , 2 4 , 2 5 }. Note that while testing one hyperparameter, we fix the others as 2 3 .…”

Section: Hyperparameter Analysismentioning

confidence: 99%

“…A popular approach is the use of federated learning (FL) to support collaborative and distributed learning processes, which enables training machine learning models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized [40]. For example, FL can be used to process EHR data distributed in multiple hospitals by sharing the local model instead of the patients' data to prevent the raw data leakage [5,6,24,33].…”

Section: Introductionmentioning

confidence: 99%

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Federated Multi-View Learning for Private Medical Data Integration and Analysis

Che¹,

Peng²,

Sun³

et al. 2021

Preprint

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Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits in medical field. Two critical challenges are identified: Firstly, medical data is naturally distributed across multiple local sites, making it difficult to collectively train machine learning models without data leakage. Secondly, in medical applications, data are often collected from different sources and views, resulting in heterogeneity and complexity that requires reconciliation. This paper aims to provide a generic Federated Multi-View Learning (FedMV) framework for multi-view data leakage prevention, which is based on different types of local data availability and enables to accommodate two types of problems: Vertical Federated Multi-View Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV). We experimented with real-world keyboard data collected from BiAffect study. The results demonstrated that the proposed FedMV approach can make full use of multi-view data in a privacy-preserving way, and both V-FedMV and H-FedMV methods perform better than their single-view and pairwise counterparts. Besides, the proposed model can be easily adapted to deal with multi-view sequential data in a federated environment, which has been modeled and experimentally studied. To the best of our knowledge, this framework is the first to consider both vertical and horizontal diversification in the multi-view setting as well as their sequential federated learning.

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Section: Methodsmentioning

confidence: 99%

Section: Hyperparameter Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Federated Multi-View Learning for Private Medical Data Integration and Analysis

Che¹,

Peng²,

Sun³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Another work in [87] focuses on complexity reduction in FL settings for EHR mortality prediction, by applying an adaptive boosting method named LoAdaBoost for increasing the efficiency of federated machine learning in both IID and non-IID data distribution scenarios. The study in [88] considers EHRs training in the FL-based healthcare with recurrent NNs (RNNs) for predicting preterm-birth 3 months using structured datasets with uncertainty training. 42 hospitals are employed in the test, and each hospital performs the update of model generalizations as the local weights in the FL process.…”

Section: Fl For Federated Ehrs Managementmentioning

confidence: 99%

“…Interestingly, the use of FL opens new opportunities for federated EHRs analytics among distributed medical institutions despite the strict privacy regulation due to its learning nature by only allowing model parameters to be exchanged while raw data are kept at local sites. From [64], [83], [88], we can find that FL is a very useful learning approach to accelerate the accuracy rates of AI model training thanks to the use of distributed data resources and computation capabilities of multiple silos. • FL is also useful for facilitating in-home health monitoring, by training a global model from distributed homes under the control of a data server, while preventing data leakage by keeping user data locally [92], [93].…”

Section: Lessons Learnedmentioning

confidence: 99%

Federated Learning for Smart Healthcare: A Survey

Nguyen¹,

Pham²,

Pathirana³

et al. 2021

Preprint

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Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.

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Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

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Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

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Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data

Cited by 10 publications

References 0 publications

Federated Multi-View Learning for Private Medical Data Integration and Analysis

Federated Multi-View Learning for Private Medical Data Integration and Analysis

Federated Learning for Smart Healthcare: A Survey

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

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