Federated Learning is an emerging technology practice that has incredible potential. It is a collaborative and decentralized approach that allows scientists to train Artificial Intelligence (AI) and Machine Learning (ML) models using sensitive data without uploading data to servers.
Even without direct data uploading, it is important to note that the Federated Learning model alone is not properly secured, and there are many opportunities for malicious activities. For this reason, conversations about Federated Learning are always centered around privacy and security and how to use data responsibly without compromising results. Many industries are exploring the possibilities that federated learning can bring to innovation, problem-solving, and digital transformation.
In healthcare, vast amounts of data are generated that could potentially be used to train deep neural networks (DNNs) for medical diagnostics and advance many other aspects of research and patient care. However, healthcare data is highly sensitive, and data owners must adhere to strict ethical guidelines along with the privacy and confidentiality standards detailed in the Health Insurance Portability and Accountability Act (HIPPA). The large size of medical imaging files poses an additional logistical challenge to collecting, sharing, and analyzing large quantities of data.
This guide examines the possibilities and challenges of using private federated learning in healthcare by defining private federated learning for healthcare, explaining how it works, and detailing its advantages.
Private Federated Learning for Healthcare Defined
Private Federated Learning is another name for Federated Learning that highlights the conversation around the robust layers of privacy protection like Differential Privacy (DP). DP is a system for publicly sharing information from datasets without sharing sensitive information. The discussions around DP and federated learning are dynamic. Critics argue that it can limit the usefulness of the data.
Private federated learning for healthcare is a collaborative learning process that allows AI and ML models to train across multiple institutions without explicitly sharing patient data. Through this way ethical guidelines and regulatory standards are fulfilled, and the models are effective for research, innovation, and patient care.
Potential Use Cases for Private Federated Learning in Healthcare
A study shows that private federated learning and DP can be applied to clinical and epidemiological research without compromising privacy or raising computational costs significantly. In this application, private federated learning and DP are used in a way that never requires the data to leave a specific device or healthcare system. The techniques are applied to many different structures and units to train the model on the complexity of tasks and diseases. Throughout the process, the raw data is stored locally and remains private to the data owner.
Another study shows how private federated learning is particularly important for histopathology departments. Histopathology images are protected by HIPPA and the GDPR. Additionally, the images are massive files that cannot easily be shared, yet significant benefits can be achieved by training models on this data. Private federated learning is a solution to both privacy and logistic challenges when it is implemented as a collaborative learning paradigm. In this approach, models are trained across multiple hospitals without sharing or storing patient data.
In both of these examples, private federated learning is instrumental in improving the accuracy and speed of diagnosis, contributing to research and knowledge sharing, and ultimately improving overall patient care.
Advantages of Private Federated Learning in the Healthcare Industry
Private federated learning has the capacity to bring many advantages to the healthcare industry worldwide. Here are three of the top benefits.
- Private federated learning leverages a broad set of data points that are crucial for building a higher-quality model. This is important because ML models must be trained on large amounts of data to support many parameters.
- All sensitive and general data is kept local to the owner and specific device. The owner maintains control over the data, and the data is kept private.
- Federated learning can drive scalable health research that has the potential to monumentally expand access to medical knowledge, lead to breakthrough discoveries, transform preventative care, and improve patient outcomes.
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