Federated Learning for Medical Imaging

The Privacy Barrier

Hospitals will not share their MRI datasets with tech companies. The regulatory hurdles (HIPAA, GDPR) and liability risks are simply insurmountable. This creates a massive data silo problem for training diagnostic AI.

Federated Averaging (FedAvg)

We bypassed this by deploying a Federated Learning architecture using Flower. Instead of moving the data to the model, we move the model to the data.

We send an initialized, untrained CNN to the hospitals' local, secure servers. They train the model on their private data for a few epochs. Then, they send ONLY the updated gradient weights back to our central server.

# Conceptual Federated Averaging
def aggregate_weights(client_weights_list):
    # client_weights_list contains the model state_dicts from hospital A, B, and C
    avg_weights = {}
    for key in client_weights_list[0].keys():
        # Average the weights across all hospitals
        avg_weights[key] = sum([client[key] for client in client_weights_list]) / len(client_weights_list)
    return avg_weights

We aggregate the gradients using Federated Averaging. The resulting global model learns from millions of diverse patient records, but the actual images and patient data never leave the hospital firewall.