Federated learning in healthcare

In the ever-evolving landscape of healthcare, advancements in technology are continuously shaping the way we approach diagnosis, treatment, and patient care. One such disruptive innovation is federated learning, a distributed machine learning approach that enables collaboration and knowledge sharing while safeguarding sensitive patient data. On our radar is the healthcare industry, recently we explored the AI application in healthcare, but today in this blog post, we will explore the concept of federated learning and its potential to transform the healthcare industry.

 

Understanding Federated Learning

Federated Learning is a decentralised learning framework that allows multiple institutions or entities to collaboratively train a shared machine learning model without sharing raw data. Instead of transferring data to a central server, the model is trained locally on each participating device or organisation, with only the model updates being shared and aggregated. This unique approach preserves data privacy and security while harnessing the collective intelligence of multiple data sources.

Healthcare institutions handle vast amounts of sensitive patient data, making privacy a top concern. Federated Learning addresses this challenge by enabling model training across multiple decentralised servers, without transferring the actual data. Instead, each institution trains a local model on its data, and only the model's updates are shared with the central server. This approach ensures that patient data remains secure within the institution's premises, reducing the risk of data breaches and maintaining compliance with stringent data protection regulations like HIPAA and GDPR.

 

Impact on stakeholders

Federated learning has a profound impact on various stakeholders within the healthcare ecosystem. Patients benefit from improved personalised treatment methodologies, as the collaborative nature of federated learning enables the development of models that encompass diverse patient populations. Healthcare providers can enhance their decision-making capabilities by leveraging shared knowledge and insights derived from federated learning models, leading to more accurate diagnoses and treatment plans. Researchers gain access to larger and more diverse datasets, enabling them to conduct studies and make discoveries that were previously limited by data availability. 

Regulatory bodies and policymakers can support the adoption of federated learning to ensure privacy protection while promoting advancements in healthcare. Ultimately, the widespread implementation of federated learning in healthcare has the potential to positively transform patient care, research outcomes, and the overall healthcare landscape.

 

Use cases of applying federated learning in healthcare

The application of federated learning in healthcare holds tremendous promise for improving patient outcomes, accelerating medical research, and enhancing clinical decision-making.

One notable example is the application of federated learning in personalised treatment methodology in radiation oncology. In this use case, our head of Data Science led the development of a groundbreaking solution that aimed to optimize personalised treatments for cancer patients. By leveraging federated learning, multiple hospitals across different countries collaborated to train a shared machine learning model without sharing sensitive patient data. Georgi Nalbantov’s AI team used Federated Learning to learn Support Vector Machine (SVM) models, using the Alternating Direction Method of Multipliers (ADMM), from disparate databases to predict treatment outcomes: which can be either a direct treatment effect or a treatment side effect, for example, shortness of breath after (lung) radiotherapy.

The performance of the SVM models was evaluated by the Area Under the Curve (AUC) in a five-fold cross-validation procedure (training at four sites and validation at the fifth). The performance of the pooled (federated) learning algorithm was compared with centralised learning, in which the datasets of all clinics are combined in a single dataset. The result of the centralised model was (naturally) the same as the federated-learning model, as they are mathematically yielding the same result. Read more in the paper here.

 

Advantages and limitations

Federated learning offers several key advantages in healthcare. Firstly, it facilitates collaboration among institutions, allowing them to pool their collective knowledge and expertise without compromising data privacy. Secondly, federated learning mitigates the risks associated with centralised data storage and transfer, reducing the likelihood of data breaches. Additionally, this approach promotes inclusivity by accommodating organisations with limited resources or regulatory constraints.

However, federated learning also faces certain limitations. One challenge is ensuring the consistency and quality of the training data across different organisations. Data heterogeneity and variations in data collection practices can introduce biases or inconsistencies in the model. Furthermore, the communication and coordination required among participating entities may pose logistical complexities.

 

Conclusion

Federated learning has the potential to revolutionise the healthcare industry by enabling collaboration, preserving data privacy, and fostering advancements in personalised treatments and medical research. As the field of healthcare continues to embrace digital transformation, the responsible implementation of federated learning holds great promise for improving patient care and driving innovation.

At VectorLabs.AI, we understand the complexities and potential of federated learning in healthcare. With our team's scientific background and hands-on expertise in AI technologies, we are well-equipped to assist your organisation in leveraging federated learning to unlock the benefits it offers. Whether you are seeking to implement federated learning solutions, optimise existing models, or navigate the challenges associated with decentralised learning, our team is here to support you. To learn more about our capabilities and discuss how federated learning can transform your organisation, write to us at [email protected].