Federated Machine Learning for Healthcare

Introduction
Traditional machine learning is based on training models on data sets that are stored in a centralized location like an on-premise server or cloud storage. For domains like healthcare, privacy and compliance issues complicate the collection,  storage, and sharing of critical patient and medical data. This poses a considerable challenge for building machine learning models for healthcare. 

Federated learning is a technique that enables collaborative machine learning without the need for centralized training data. A shared machine learning model is trained by keeping all the training data on a device, thereby ensuring higher levels of privacy and security compared to the traditional machine learning setup where data is stored in the cloud.

This technique is especially useful in domains with high security and privacy constraints like healthcare, finance, or governance. Users benefit from the power of personalized machine learning models without compromising their sensitive data. 

This article describes federated learning and its various applications with a special focus on healthcare. 

How Does Federated Learning Work?
This section discusses in detail how federated learning works for a hypothetical use case of a number of healthcare institutions working collaboratively to build a deep learning model to analyze MRI scans. 

In a typical federated learning setup, there’s a centralized server, for instance, in the cloud, that interacts with multiple sources of training data, such as hospitals in this example. The centralized server houses a global deep learning model for the specific use case that is copied to each hospital to train on its own data set. 

Each hospital in this setup trains the global deep learning model locally for a few iterations on its internal data set and sends the updated version of the model back to the centralized server. 

Each model update is then sent to the cloud server using encrypted communication protocols, where it’s averaged with the updates from other hospitals to improve the shared global model. The updated parameters are then shared with the participating hospitals so that they can continue local training.

In this fashion, the global model can learn the intricacies of the diverse data sets stored across various partner hospitals and become more robust and accurate. At the same time, the collaborating hospitals never have to send their confidential patient data outside their premises, which helps ensure that they don’t violate strict regulatory requirements like HIPAA. The data from each hospital is secured within its own infrastructure. 

This unique federated learning setup is easily scalable and can accommodate new partner hospitals; it also remains unaffected if any of the existing partners decide to exit the arrangement.

Use Cases for Federated Learning in Healthcare
Federated learning has immense potential across many industries, including mobile applications,  healthcare, and digital health. It has already been used successfully for healthcare applications, including health data management, remote health monitoring, medical imaging, and COVID-19 detection.

As an example of its use for mobile applications, Google used this technique to improve Smart Text Selection on Android mobile phones. In this use case, it enables users to select, copy, and use text quickly by predicting the desired word or sequence of words based on user input. Each time a user taps to select a piece of text and corrects the model’s suggestion, the global model receives precise feedback that’s used to improve the model. 

Federated learning is also relevant for autonomous vehicles to improve real-time decision-making and real-time data collection about traffic and roads. Self-driving cars require real-time updates, and the above types of information can be effectively pooled from several vehicles in real time using federated learning. 

Privacy and Security
With increased focus on data privacy laws from governments and regulatory bodies, protecting user data is of utmost importance. Many companies store customer data, including personally identifiable information such as names, addresses, mobile numbers, email addresses, etc. 

Apart from these static data types, user interactions with companies such as chat, emails, and phone calls also carry sensitive details that need to be protected from hackers and malicious attacks. 

Privacy-enhancing technologies like differential privacy, homomorphic encryption, and secure multi-party computation have advanced significantly and are used for data management, financial transactions, and healthcare services, as well as data transfer between multiple collaborative parties. 

Many startups and large tech companies are investing heavily in privacy technologies like federated learning to ensure that customers have a pleasant user experience without their personal data being compromised.

In the healthcare industry, federated learning is a promising technology that allows, for example, hospitals to share electronic health records (EHR) to create more accurate models. Privacy is preserved without violating strict HIPAA standards by decentralizing the data processing, which is distributed among multiple end-points instead of being managed from a central server.

Simply put, federated learning allows training of machine learning models without the need to collect raw data in a central location; instead, the data used by each end-point (in this example, hospitals) remains local. By combining the above with differential privacy, hospitals can even provide a quantifiable measure of data anonymization.

Federated Learning vs. Distributed Learning and Edge Computing
Federated learning is often confused with distributed learning. In the context of deep learning, distributed training is used to train large, deep neural networks across a number of GPUs or machines. However, distributed learning relies on centralized training data shared across multiple nodes to increase the speed of model training. 

Federated learning, on the other hand, is based on decentralized data stored across a number of devices and produces a central, aggregate model. A fascinating example of the potential of this technology is using federated learning-based Person Movement Identification (PMI) through wearable devices for smart healthcare systems.

Edge computing is a related concept where the data and model are centralized in the same individual device. Edge computing doesn’t train models that learn from data stored across multiple devices, as in the case of federated learning. Instead, a centrally trained model is deployed on an edge device, where it runs on data collected from that device. For example, edge computing is applied in the context of Amazon Alexa devices, where a wake word detection model is stored on the device to detect every utterance of “Alexa.”

AI and Healthcare
Federated machine learning has a strong appeal for healthcare applications. By design, patient and medical data is highly regulated and needs to adhere to strict security and privacy standards. By collating data from participating healthcare institutions, organizations can ensure that confidential patient data doesn’t leave their ecosystem; they can also benefit from machine learning models trained on data across a number of healthcare institutions. 

Large hospital networks can now work together and pool their data to build AI models for a variety of medical use cases. With federated learning, smaller community and rural hospitals with fewer resources and lower budgets can also benefit and provide better health outcomes to more of the population. 

This technique also helps to capture a greater variety of patient traits, including variations in age, gender, and ethnicity, which may vary significantly from one geographic region to another. Machine learning models based on such diverse data sets are likely to be less biased and more likely to produce more accurate results.  In turn, the expert feedback of trained medical professionals can help to further improve the accuracy of the various AI models.

Federated learning, therefore, has the potential to introduce massive innovations and discoveries in the healthcare industry and bring novel AI-driven applications to market and patients faster.

Conclusion 
Federated learning enables secure, private, and collaborative machine learning where the training data doesn’t leave the user device or organizational infrastructure. It harnesses diverse data from various sources and produces an aggregate model that’s more accurate. 

This technique has introduced significant improvements in information sharing and increased the efficacy of collaborative machine learning between hospitals. It circumvents and overcomes the challenges of working with highly sensitive medical data while leveraging the power of state-of-the-art machine learning and deep learning. 

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