Healthcare AI isn’t stalling for lack of algorithms or data - it’s stalling because the data isn’t free.
The most valuable signals in biomedicine - omics, imaging, clinical records, sit locked inside institutional silos. Centralized ML approaches quickly hit walls: regulatory hurdles, privacy risks, and poor generalizability across sites. The real bottleneck isn’t model design, it’s AI data readiness -curating, normalizing, and building context-aware features at scale without breaking compliance or burning through resources.

This whitepaper shows how federated learning (FL) lets institutions train models locally and share only learned parameters - not raw data - while applying domain-specific preprocessing at each node and maintaining full auditability. By keeping data local and eliminating cross-border transfers, it aligns with HIPAA/GDPR and preserves data sovereignty.

The result is practical, production-ready collaboration: faster iteration, lower infrastructure/compliance burden, and better generalizability across institutions - supported by real-time observability and audit trails.

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In this whitepaper, you’ll learn how to:

• Collaborate without sharing raw data. Train models locally and exchange only model parameters, aligning with HIPAA/GDPR and preserving data sovereignty - with full audit trails via Polly.
• Deploy a production-ready FL stack. Use an AWS + Flower architecture (private subnets, encrypted S3, VPC peering, least-privilege IAM) and FedAvg-based aggregation designed for secure, multi-site training.
• Make biomedical data AI-ready at the edge. Apply domain-specific preprocessing and harmonization - tissue segmentation, stain normalization, patch extraction; ComBat-seq for RNA-seq; OMOP for clinical - to improve cross-site robustness.
• Gain real-time visibility and trust. Leverage centralized dashboards for loss/accuracy and epoch-level traces, plus CloudWatch audit logging; track drift/fairness to satisfy partner governance.
• Scale collaborations quickly. Onboard new institutions via automated templates, support heterogeneous environments with gRPC, and autoscale compute as workloads grow.
• See it working in practice. A multi-site precision-oncology case study (gene-expression prediction from WSIs across three participants) achieved meaningful performance with zero data-exposure events - fully observable and compliant.

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