Editing Federated Learning (section)

=== 5.7.1 Healthcare ===

Healthcare is one of the most impactful and widely studied application domains for Federated Learning (FL), particularly due to its stringent privacy requirements and highly sensitive patient data. Traditional machine learning models typically require centralizing large amounts of medical data—ranging from diagnostic images and electronic health records (EHRs) to genomic sequences—posing serious risks under regulations like HIPAA and GDPR. FL addresses this challenge by enabling multiple hospitals, clinics, or wearable devices to collaboratively train models while keeping all patient data on-site<sup>[1][3]</sup>.

One notable example is the application of FL to train predictive models for disease diagnosis using MRI scans or histopathology images across multiple hospitals. In these setups, each institution trains a model locally using its patient data and only shares encrypted or aggregated model updates with a central aggregator. This approach has been successfully used in training FL-based models for COVID-19 detection, brain tumor segmentation, and diabetic retinopathy classification<sup>[1][2]</sup>. The benefit lies in improved model generalization due to access to diverse and heterogeneous datasets, without the legal and ethical complications of data sharing.

In addition to institutional collaboration, FL is also used in consumer health scenarios such as smart wearables. Devices like smartwatches and fitness trackers continuously collect user health data (e.g., heart rate, blood pressure, activity logs) that can be used to train personalized health monitoring systems. FL allows these models to be trained locally on-device, thereby reducing cloud dependency and latency while preserving individual privacy<sup>[3]</sup>.

Challenges in this domain include handling non-IID data distributions across institutions, varied device capabilities, and communication constraints. Techniques like personalization layers, hierarchical FL, and secure aggregation protocols are often integrated into healthcare FL deployments to overcome these issues<sup>[4][5]</sup>. As the need for predictive healthcare analytics grows, FL is becoming foundational to building AI systems that are not only accurate but also ethically and legally compliant in multi-party medical environments.