Larynx cancer survival model developed through open-source federated learning

Hansen, Christian Rønn; Price, Gareth J; Field, Matthew; Sarup, Nis; Zukauskaite, Ruta; Johansen, Jørgen; Eriksen, Jesper Grau; Aly, Farhannah; McPartlin, A.; Holloway, Lois; Thwaites, David; Brink, Carsten

doi:10.1016/j.radonc.2022.09.023

Cited by 23 publications

(8 citation statements)

References 31 publications

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“…T ρ T ρ+F η [10], [38], [40], [47], [28], [64] , [35], [29], [85], [9], [25] [51], [17], [45], [50] Precision T ρ T ρ+F ρ [10], [40], [47], [28], [64], [35], [29], [85], [9], [25] [51], [17], [50] Specificity T η T η+F ρ [51], [17], [45] F1-Score 2 * P recision * Recall P recision+Recall [10], [40], [4], [47], [28], [64], [35], [29], [85], [9], [17] ROC-AUC Score b a f (x)dx [10], [35] , [9] PR-AUC Score b a f (x)dx [10], [36], [22] Root Mean Square Error (RMSE)…”

Section: Measures Equation References Accuracymentioning

confidence: 99%

Role of federated learning in healthcare systems: A survey

Rana¹,

Marwaha²

2024

MFC

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Nowadays, machine learning affects practically every industry, but the effectiveness of these systems depends on the accessibility of training data sets. Every device now produces data, and that data can serve as the foundation for upcoming technologies. Traditional machine learning systems need centralised data for their training, but the availability of valid and good amounts of data is not always possible due to various privacy risks. But federated learning can solve this issue [78]. In a federated learning (FL) environment, a model can be trained on decentralised datasets by involving a large number of participants, such as mobile devices or entire enterprises. Researchers are using this technique in various fields and getting great responses. The importance of using federated learning in the healthcare industry is highlighted in this paper since there is a wealth of data available in hospitals or electronic health records that may be used to train medical systems but cannot be shared due to privacy issues. The main contribution of this paper is to highlight the role of federated learning in the medical field. It also presents a list of frameworks available to implement federated learning models. The paper also listed the evaluation metrics used to check the efficiency of a federated learning model. Broadly used evaluation metrics are accuracy, precision, recall, and F1-score. Open issues for research in this area are also discussed at the end of this paper.

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Section: Measures Equation References Accuracymentioning

confidence: 99%

Role of federated learning in healthcare systems: A survey

Rana¹,

Marwaha²

2024

MFC

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“…Hansen et al [90] created a stratified Cox regression model using data from hospitals in three countries, ensuring that patient-specific information remained within the hospital premises to avoid any data leakage risks. The key factors influencing the survival model are tumor volume and performance status.…”

Section: Federated Learning Approachmentioning

confidence: 99%

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Upreti,

Yang,

Kim

et al. 2024

CMES

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Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources, such as patient records, medical imaging, wearable devices, and clinical research surveys. This research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in healthcare. It evaluates the effectiveness of federated learning strategies in the field of healthcare. It offers a systematic analysis of federated learning in the healthcare domain, encompassing the evaluation metrics employed. In addition, this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.

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“…FL has already been proven to work well in many other domains, e.g., cancer research [65], natural language processing [49], graph NNs [27], image classification [53], transfer learning [52], language models [3], mobile keyboard prediction [25], and keyword spotting [46], so it is reasonable to anticipate that it is likewise effective in the domain of RecSys's. In fact, there are numerous methods in the literature to incorporate current RecSys frameworks into FL.…”

Section: Federated Recommender Systemsmentioning

confidence: 99%

A Privacy Preserving System for Movie Recommendations using Federated Learning

Neumann¹,

Lutz²,

Müller³

et al. 2023

Preprint

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Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are employed by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles within social networks, recommender systems are often reproved for collecting considerable amounts of personal data. However, to personalize recommendations, personal information is fundamentally required. A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection. Accordingly, we present a complete recommender system for movie recommendations, which provides privacy and thus trustworthiness on two levels: First, it is trained using federated learning and thus is, by its very nature, privacy-preserving, while still enabling individual users to benefit from global insights. And second, a novel federated learning scheme, FedQ, is employed, which not only addresses the problem of non-i.i.d. and small local datasets, but also prevents input data reconstruction attacks by aggregating client models early. To reduce the communication overhead, compression is applied, which significantly reduces the exchanged neural network updates to a fraction of their original data. We conjecture that it may also improve data privacy through its lossy quantization stage.

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Larynx cancer survival model developed through open-source federated learning

Cited by 23 publications

References 31 publications

Role of federated learning in healthcare systems: A survey

Role of federated learning in healthcare systems: A survey

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

A Privacy Preserving System for Movie Recommendations using Federated Learning

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