Healthcare AI Training Platform Comparison
You have options. Most of them have structural problems.
The healthcare AI training platform landscape divides into four architectural categories: cloud BAA (copy data to cloud), federated learning (send gradients to aggregator), confidential cloud computing (TEE in cloud provider infrastructure), and compute-to-data (full training at the hospital edge). Each has fundamentally different security, compliance, and ML utility characteristics.
Security posture comparison
Cloud BAA (AWS HealthLake, GCP Healthcare API, Azure Health Data Services)
- Data location: Cloud provider infrastructure
- Data movement required: Yes — data export to cloud
- Attack surface: Cloud provider, all network hops, all storage replicas
- Hardware attestation: No (data visible to cloud operator)
- Network isolation during training: No
- Gradient transmission: Not applicable (centralised)
Federated Learning (NVIDIA FLARE, Rhino Health, Owkin, Apheris)
- Data location: Each institution's site
- Data movement required: No — raw data stays local
- Attack surface: Central aggregator, gradient transmission path
- Hardware attestation: Varies by implementation (most: no)
- Network isolation during training: No (must communicate with aggregator)
- Gradient transmission: Yes — invertible to training data
Confidential Cloud Computing (BeeKeeperAI, Azure confidential computing)
- Data location: Cloud provider's TEE infrastructure
- Data movement required: Yes — data export to cloud TEE
- Attack surface: Cloud provider (enclave host), attestation pipeline
- Hardware attestation: Yes — cloud-managed DCAP
- Network isolation during training: No (cloud network)
- Gradient transmission: Not applicable (centralised)
Compute-to-Data (Rapha Protocol)
- Data location: Hospital's own infrastructure
- Data movement required: No — compute moves to data
- Attack surface: Single TEE enclave, no network path during training
- Hardware attestation: Yes — SGX/DCAP + TPM 2.0, independently verified
- Network isolation during training: Yes — Rust kernel air-gap severs WAN
- Gradient transmission: None — full training, only weights leave
ML utility comparison
Cloud BAA
Full access to centralised dataset. Can run any training paradigm. Best ML utility — at the cost of maximum PHI exposure.
Federated Learning
Partial training per site. Non-IID data degrades convergence. Gradient leakage limits what models can safely be trained. ML quality trades off against privacy.
Confidential Cloud
Full training in cloud TEE. Can run any paradigm. ML utility is high. But data must leave the hospital to enter the cloud enclave — trust shifts to cloud provider.
Compute-to-Data
Full training at the edge. Can run any ML framework inside SGX/TDX enclave. No convergence issues from non-IID data across sites. Full-batch training on actual dataset. Best ML utility with zero PHI exposure risk.
Total cost of ownership
Cloud BAA
Pay for cloud compute, storage, data transfer, and BAA overhead. Ongoing data residency costs. Liability risk if cloud provider is breached — your BAA shifts responsibility, not consequences.
Federated Learning
Pay for FL platform license, site coordination engineering, network infrastructure, and aggregation server compute. Ongoing coordination overhead. Engineering cost of managing non-IID convergence and gradient leakage mitigation.
Confidential Cloud
Pay for confidential VM instances (premium over standard VMs), data transfer into cloud, attestation infrastructure. Still paying the cloud provider — and trusting them with your attestation pipeline.
Compute-to-Data
Pay per training job — not per hour, not per GPU. USDC escrow settles on Polygon only after verified training. Hospital earns 70%, node owner 20%, onboarder 5%, protocol 5%. No cloud infrastructure cost. No data transfer cost. No engineering cost to manage FL coordination.
Rapha Protocol is private-alpha. This comparison is based on public documentation and published research. Platform capabilities may change. Evaluate all platforms independently for your specific security, regulatory, and ML requirements.