How healthcare consortia and financial institutions are collaboratively training state-of-the-art machine learning models without sharing proprietary raw training data.
Hospitals holding rare disease patient records or banks tracking international fraud cartels cannot legally or competitively pool their sensitive training datasets. Federated learning partially addresses this, but model gradient updates can still leak training data through inversion attacks.
We explain how to train convolutional and transformer models over CKKS-encrypted tensors. Linear layers (matrix multiplications) execute natively, while non-linear activations (ReLU) are approximated with low-degree polynomial polynomials, preserving gradient descent convergence.
Real-world performance metrics from a medical imaging consortium, demonstrating that hardware-accelerated FHE clusters can train diagnostic classification models in hours rather than months.