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Federated Learning

BloodCounts! is comprised of many collaborators in different countries. Each participating hospital contributes unique data reflective of their diverse patient demographics and health conditions. This data diversity means we can develop Machine Learning (ML) models for a comprehensive understanding of patient health across various populations. However, this is a new area of machine learning, and so we are developing new approaches to meet these challenges:

  1. Secure Data Sharing: Establishes methods for transferring data between institutions while ensuring patient privacy is maintained.

  2. Managing Diverse Data: Capable of processing data from multiple sources, accommodating differences in format and quality.

  3. Regulatory Compliance: Adheres to data protection regulations such as GDPR and HIPAA.

  4. Scalability and Flexibility: Equipped to handle large amounts of data and adaptable to different healthcare settings.

  5. Collaborative Learning: Enables hospitals to share insights without the need to exchange raw data, guaranteeing the model's applicability across various patient groups and healthcare environments.

Convential ML approaches, which typically involve centralising data for analysis, present significant challenges including risks to data privacy, high costs, scalability issues, and regulatory compliance concerns. To address these issues, BloodCounts! is exploring an alternative method.

Federated Learning (FL) is an advanced ML technique that allows multiple entities, such as hospitals, to jointly train a model without the necessity of sharing their data. This method is particularly valuable in ensuring data privacy and security. However, the practical application of FL in healthcare has been limited, mainly due to challenges in deployment and managing sensitive data across diverse healthcare systems.

BloodCounts! is advancing FL to a stage where it can be effectively utilised for our diverse and remote datasets.

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