A machine learning (ML) framework called Federated Learning (FL) allows clients located in various geographic regions to work together to train a common model without explicitly sharing local data. It can be applied to the development of new systems as well as to improve the performance of current ones.
Due to its capacity to give organizations superior insights into the medical histories and treatment plans of their patients, federated learning has been increasingly popular in healthcare organizations in recent years. FL protects patient privacy by enabling healthcare organizations to collaborate on training machine learning models while maintaining localized and private raw data. This method has been used in many areas of healthcare, especially in the diagnosis of illnesses.
Federated learning: What is it?
A novel training method called federated learning involves applying machine learning models that have been trained on one dataset to another. Issues such as class disparities and non-IID (non-independent and identically distributed) data distributions among clients are addressed by this methodology. These problems may lead to slower convergence and less-than-ideal model performance, which is known as the “client-drift” problem. It is significantly simpler and less expensive to create and maintain because it functions similarly to a single model that can be used on an infinite number of distinct datasets. There are countless uses for this technology, and we will examine a few of them here!
What is FL in Healthcare?
Due to its use in data privacy, which is crucial for managing sensitive patient medical data like reports, pictures, and electronic health records (EHRs), FL is growing in popularity in the healthcare industry. FL protects patient privacy by enabling healthcare organizations to collaborate on training machine learning models while maintaining localized and private raw data. The precision of identifying diseases, including cancer, diabetic retinopathy, and others has increased thanks to models trained on a variety of multi-institutional datasets.
Furthermore, FL has made it possible for EHR data to be shared securely, supporting the creation of patient outcome prediction models and individualized treatment programs while upholding privacy rules. Additionally, FL has made it easier to analyze data from wearable devices, which has helped in the early detection and tracking of chronic conditions like diabetes and heart disease.
Types of Federated Learning
1. Horizontal Federated Learning
A distributed machine learning approach called horizontal federated learning (HFL) uses data from dispersed devices or nodes to improve model performance. This method allows for collaborative learning while maintaining data confidentiality and privacy, and it is frequently used in situations involving mobile devices or Internet of Things (IoT) devices.
This decentralized method addresses serious privacy and data security concerns by lowering the chance of data breaches and guaranteeing that private information stays on users’ devices. Compared to centralized ML techniques, HFL has the advantage of being able to use a broader and more varied dataset. HFL makes it possible to train models on a wide variety of data sources by combining data from several nodes, which improves accuracy and resilience.
2. Vertical Federated Learning
By utilizing data from several dispersed nodes or devices, Vertical Federated Learning (VFL) seeks to improve the effectiveness of machine learning models. In industries like healthcare and finance, where heterogeneous data is frequently collected from many devices and nodes, VFL is widely used. VFL makes it possible to aggregate medical records from multiple patients in the healthcare industry, providing distinct data for model training.
Vertical data partitioning is used in the pre-processing stage of VFL to separate data from each node into discrete portions that correspond to particular data categories. In VFL, ensuring uniformity and excellent performance across all nodes is a major problem. VFL usually uses data refinement methods and quality control procedures to overcome this issue.
Applications of federated learning in healthcare
There are numerous applications for federated learning in the healthcare sector:
1. Precision medicine:
An experimental study that creates a customized treatment plan for a single patient using personal data is known as precision medicine. In addition to predicting patient outcomes or the course of an illness, federated learning may also anticipate how a patient will react to medication or other therapies. Researchers may be able to find novel molecular treatments for diseases like cancer and Alzheimer’s disease by using federated learning to evaluate vast volumes of patient data. This could result in better individualized care for individuals suffering from these illnesses.
2. Electronic Medical Record
FL can greatly improve clinical decision-making in EHR systems. FL, which was first put up by Google to suggest board questions, entails training a global model with information from multiple sources, such as wearable technologies, hospital systems, and personal medical records.
By enabling insights and predictions based on a larger and more varied dataset without transferring the data, FL solves the problem of data silos in EHR systems. The importance of EHRs in healthcare is discussed by Kuliha et al. (2024), who concentrate on the difficulties associated with data protection and administration employing FL and blockchain technology. They address problems such as encouraging FL involvement, guaranteeing precise model aggregation, and handling the substantial amount of EHR data.
3. Research:
Because it facilitates the creation of sizable patient data sets that researchers may utilize for their studies without requiring direct access to any individual’s private health information (PHI), federated learning has been applied in medical research. Moreover, it enables researchers who might not otherwise have access to specific medical records or resources (such as genetic data on patients) to do so by using the databases of other researchers. By doing this, privacy violations are avoided, yet researchers are still able to obtain sufficient data and insights.
4. Breast Density Classification:
Determining a patient’s risk of breast cancer requires classifying their breast density. FL is a potential new technology that could improve breast density categorization. Data from many hospitals or imaging centers may be combined while maintaining the anonymity of the individual datasets in order to train an ML model for breast density classification. The open-source program MammoDL calculates the density and complexity of breast tissue from mammograms using an FL approach and the U-Net DL architecture.
In order to help overcome this challenge, FL can combine smaller datasets from multiple universities into a larger, more diverse dataset. FL may also be helpful with privacy concerns. A typical ML situation requires all data to be centralized, which leaves it vulnerable to hackers and data breaches.
A Revolutionary Influence on healthcare through federated learning
Let’s examine more closely how federated learning models are being adopted by healthcare organizations to improve patient care, marking a revolutionary change. These are a few notable applications:
1. Using predictive prevention to foresee potential health hazards
These models provide proactive interventions that prolong people’s health by identifying possible health hazards before they become more serious through the analysis of decentralized data. The healthcare industry is looking forward to a time when medical insights are more acute, therapies are more successful, and prevention becomes a proactive game-changer—all while protecting patient privacy—thanks to federated learning.
2. Supercharged illness detection through early diagnosis
By using federated learning, healthcare professionals are transforming diagnostics and achieving previously unheard-of levels of disease detection accuracy. These models go beyond conventional methods, allowing for earlier identification and more accurate therapies, giving medical personnel more confidence to address health issues.
3. Customized care: the pinnacle of accuracy
The creation of customized therapy models that maximize patient outcomes is being propelled by deep learning. The finest aspect? In addition to improving care, this technology dramatically lowers medical expenses, increasing the effectiveness and accessibility of healthcare.
To Conclude
By facilitating safe and cooperative AI model training without compromising patient privacy, federated learning (FL) is revolutionizing the healthcare industry. FL enhances disease diagnostics, patient outcome forecasts, and individualized treatment regimens by utilizing decentralized data from several institutions. It is essential for precision medicine, early detection, and effective use of electronic health records (EHRs).
Its adaptability is demonstrated by applications such as wearable data analysis and breast density categorization. In the end, FL represents a major step toward more intelligent, secure, and patient-centered healthcare systems by improving the accuracy of healthcare models while simultaneously lowering expenses and protecting private data.
