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The success of machine learning-based studies is largely subjected to accessing a large amount of data. However, accessing such data is typically not feasible within a single health …
In general, through systematic categorization and analysis of existing FL systems, we offer insights to design efficient, accurate, and privacy-preserving healthcare applications using …
Jan 1, 2025 · At the same time, unsupervised models, including clustering and dimensionality reduction methods, are valuable in settings where labeled data is scarce or …
Jul 17, 2025 · Federated learning (FL) emerges as a transformative alternative, enabling the training of machine learning models across multiple institutions without requiring the exchange …
Oct 15, 2024 · This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, …
Federated learning is a useful tool in healthcare because it guarantees the privacy and security of patient data. Our results demonstrated the ability of aggregated models to predict mortality and ...
Feb 15, 2024 · This paper proposes a secure federated learning system based on data fusion using multi-party computation and additive secret-sharing methods. The gradient parameters …
To address these challenges, federated data access and federated learning (FL) offer innovative solutions. Federated data access enables the analysis and extraction of insights from health …
Dec 4, 2024 · Consequently, federated learning (FL) has emerged as a trending solution [2] to address data privacy concerns in the healthcare industry, enabling research collaborations …
Predictive analysis helps clinicians anticipate patient health paths based on historical and real-time data, therefore facilitating initial interventions. AI-based algorithms can identify patients in …
As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that …
Apr 11, 2023 · In 1976, philosophy professor Patrik Hill proposed the term FL to form a Federated Learning Community (FLC) for bringing people from different re- search universities to learn …
3 days ago · This study aimed to design a desktop application that implements machine learning algorithms to predict dental treatment time durations, assess the accuracy of the model, and …
Federated learning is a decentralized approach to training machine learning (ML) models. Each node across a distributed network trains a global model using its local data, with a central …
Nov 1, 2024 · In recent years, Big Data Analytics (BDA) and Federated Learning (FL) have become increasingly essential in healthcare, potentially revolutionizing patient care and …
We summarized the general solution to the challenges in federated learning scenario and surveyed a set of representative federated learning methods for healthcare. In the last part of …
Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients) collaboratively train a model while keeping …
Jul 11, 2025 · A new method improves the prediction of hospital stay lengths while protecting patient privacy. Predicting how long a patient stays in the hospital is...
Federated learning can accommodate this heterogeneity by training models on data distributed across various locations. For example, one hospital may specialize in cancer treatment, while …
Nov 1, 2022 · Hence, this study proposes a federated machine learning-based model for forecasting patients’ LOS in days combining the results from locally trained models of various …
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