Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions

Kanani, Pallika; Marathe, Virendra J.; Peterson, Daniel W.; Harpaz, Rave; Bright, S.

doi:10.1007/978-3-030-93733-1_37

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…[ [59][60][61] Free text Vaccine adverse events, -Named entity recognition, entity recognition, and relation extraction tasks were performed with FL. It was demonstrated that natural language processing models can be built using FL.…”

Section: Data Typesmentioning

confidence: 99%

“…The use of free text in studies was primarily associated with natural language processing, as evidenced by six studies. These investigations encompassed a range of applications: a violence risk assessment [57], benchmarking bidirectional encoder representations from transformers (BERT) models [58], a named entity recognition task [59], detecting adverse events related to vaccines [60], developing a medical relation extraction model [61], and creating a deep learning-based personalized clinical decision support system [62].…”

Section: Data Typesmentioning

confidence: 99%

“…To protect against security threats, measures such as differential privacy and homomorphic encryption can be implemented. Our review of FL studies revealed that 11 papers [21,24,28,30,35,48,49,58,60,73,78] employed differential privacy as a security measure, while four papers [36,48,66,67] utilized homomorphic encryption. Differential privacy emerged as the most commonly adopted security measure.…”

Section: ) Countermeasures Against Security Issuesmentioning

confidence: 99%

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Survey of Medical Applications of Federated Learning

Choi,

Cha,

Lee

et al. 2024

Healthc Inform Res

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Objectives: Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain.Methods: We conducted a literature search using the keywords "federated learning" in combination with "medical," "healthcare," or "clinical" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security.Results: In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns.Conclusions: FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.

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Section: Data Typesmentioning

confidence: 99%

Section: Data Typesmentioning

confidence: 99%

Section: ) Countermeasures Against Security Issuesmentioning

confidence: 99%

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Survey of Medical Applications of Federated Learning

Choi,

Cha,

Lee

et al. 2024

Healthc Inform Res

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Federated learning-based natural language processing: a systematic literature review

Khan,

Sánchez,

Domingo-Ferrer

2024

Artif Intell Rev

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Federated learning (FL) is a decentralized machine learning (ML) framework that allows models to be trained without sharing the participants’ local data. FL thus preserves privacy better than centralized machine learning. Since textual data (such as clinical records, posts in social networks, or search queries) often contain personal information, many natural language processing (NLP) tasks dealing with such data have shifted from the centralized to the FL setting. However, FL is not free from issues, including convergence and security vulnerabilities (due to unreliable or poisoned data introduced into the model), communication and computation bottlenecks, and even privacy attacks orchestrated by honest-but-curious servers. In this paper, we present a systematic literature review (SLR) of NLP applications in FL with a special focus on FL issues and the solutions proposed so far. Our review surveys 36 recent papers published in relevant venues, which are systematically analyzed and compared from multiple perspectives. As a result of the survey, we also identify the most outstanding challenges in the area.

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Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

Patel¹,

Bhattacharya

Tanwar

et al. 2022

IEEE Access

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The smart healthcare system has improved the patients quality of life (QoL), where the records are being analyzed remotely by distributed stakeholders. It requires a voluminous exchange of data for disease prediction via the open communication channel, i.e., the Internet to train artificial intelligence (AI) models efficiently and effectively. The open nature of communication channels puts data privacy at high risk and affects the model training of collected data at centralized servers. To overcome this, an emerging concept, i.e., federated learning (FL) is a viable solution. It performs training at client nodes and aggregates their results to train the global model. The concept of local training preserves the privacy, confidentiality, and integrity of the patient's data which contributes effectively to the training process.The applicability of FL in the healthcare domain has various advantages, but it has not been explored to its extent. The existing surveys majorly focused on the role of FL in diverse applications, but there exists no detailed or comprehensive survey on FL in healthcare informatics (HI). We present a relative comparison of recent surveys with the proposed survey. To strengthen healthcare data privacy and increase the QoL of patients, we proposed an FL-based layered healthcare informatics architecture along with the case study on FLbased electronic health records (FL-EHR). We discuss the emerging FL models, and present the statistical and security challenges in FL adoption in medical setups. Thus, the review presents useful insights for both academia and healthcare practitioners to investigate FL application in HI ecosystems.

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Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions

Cited by 4 publications

References 21 publications

Survey of Medical Applications of Federated Learning

Survey of Medical Applications of Federated Learning

Federated learning-based natural language processing: a systematic literature review

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

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