Federated Transfer Learning for EEG Signal Classification

Chen, Ju; Gao, David Yang; Mane, Ravikiran; Tan, Ben; Liu, Yang; Guan, Cuntai

doi:10.1109/embc44109.2020.9175344

Cited by 94 publications

(40 citation statements)

References 18 publications

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“…to a wide variety of tasks in medicine in: 1) overcoming data interoperability standards that would usually prohibit sensitive health data from being shared: 2) eliminating low data regimes of clinical machine learning tasks that predicting rare diseases (Figure 4a) 85,[166][167][168][169][170][171][172][173][174][175][176][177][178][179][180] . For example, in EMR data, federated learning has been previously demonstrated in satisfying privacy-preserving guarantees for transferring sensitive health data, as well as developing early warning systems for hospitalization, sepsis, and other preventive tasks 175,181 .…”

Section: Paths Forward Distributed Learning To Overcome Unfair Datase...mentioning

confidence: 99%

Algorithm Fairness in AI for Medicine and Healthcare

Chen¹,

Chen²,

Lipková³

et al. 2021

Preprint

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In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent evaluation of AI models stratified across race sub-populations have revealed enormous inequalities in how patients are diagnosed, given treatments, and billed for healthcare costs. In this perspective article, we summarize the intersectional field of fairness in machine learning through the context of current issues in healthcare, outline how algorithmic biases (e.g. -image acquisition, genetic variation, intra-observer labeling variability) arise in current clinical workflows and their resulting healthcare disparities. Lastly, we also review emerging strategies for mitigating bias via decentralized learning, disentanglement, and model explainability.

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Section: Paths Forward Distributed Learning To Overcome Unfair Datase...mentioning

confidence: 99%

Algorithm Fairness in AI for Medicine and Healthcare

Chen¹,

Chen²,

Lipková³

et al. 2021

Preprint

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“…Chen et al [39] explores transfer learning to improve the accuracy of global models trained to analyze IoHT data. Ju et al [40] explores transfer learning to improve the accuracy of models to analyze EEG (electroencephalogram) data to enable brain-computer interfaces. Zhang et al [41] proposes an FL tailored to train models and analyze ECG (electrocardiogram) data to diagnose arrhythmia using IoHT devices.…”

Section: Casementioning

confidence: 99%

Federated Learning for Healthcare: Systematic Review and Architecture Proposal

Antunes

Costa

Küderle

et al. 2022

ACM Trans. Intell. Syst. Technol.

240

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The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods require training of high-quality learning models based on diverse and comprehensive datasets, which are hard to obtain due to the sensitive nature of medical data from patients. In this context, federated learning (FL) is a methodology that enables the distributed training of machine learning models with remotely hosted datasets without the need to accumulate data and, therefore, compromise it. FL is a promising solution to improve ML-based systems, better aligning them to regulatory requirements, improving trustworthiness and data sovereignty. However, many open questions must be addressed before the use of FL becomes widespread. This article aims at presenting a systematic literature review on current research about FL in the context of EHR data for healthcare applications. Our analysis highlights the main research topics, proposed solutions, case studies, and respective ML methods. Furthermore, the article discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review. The collected literature corpus indicates that there is extensive research on the privacy and confidentiality aspects of training data and model sharing, which is expected given the sensitive nature of medical data. Studies also explore improvements to the aggregation mechanisms required to generate the learning model from distributed contributions and case studies with different types of medical data.

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“…A model improvement on the target by means of FTL is achieved by creating a shared representation keeping the federation of data. For example via manifold alignment [18] or domain adversarial learning [19]. Alternatively, FTL methods improve the target model leveraging the source model by instance reweighting [20] or by receiving gradients from source [17].…”

Section: Transfer Learningmentioning

confidence: 99%

Federated Learning - Methods, Applications and beyond

Heusinger¹,

Raab²,

Rossi³

et al. 2021

ESANN 2021 Proceedings

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In recent years the applications of machine learning models have increased rapidly, due to the large amount of available data and technological progress. While some domains like web analysis can benefit from this with only minor restrictions, other fields like medicine with patient data are stronger regulated. In particular data privacy plays an important role as recently highlighted by the trustworthy AI initiative of the EU or general privacy regulations in legislation. Another major challenge is, that the required training data is often distributed in terms of features or samples and unavailable for classical batch learning approaches. In 2016 Google came up with a framework, called Federated Learning to solve both of these problems. We provide a brief overview on existing Methods and Applications in the field of vertical and horizontal Federated Learning, as well as Federated Transfer Learning.

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Federated Transfer Learning for EEG Signal Classification

Cited by 94 publications

References 18 publications

Algorithm Fairness in AI for Medicine and Healthcare

Algorithm Fairness in AI for Medicine and Healthcare

Federated Learning for Healthcare: Systematic Review and Architecture Proposal

Federated Learning - Methods, Applications and beyond

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