Why Confidential Training Matters
Centralized training infrastructure exposes datasets and model IP. Phala enables consortium learning and regulated-industry training with hardware isolation.
Training data contains regulated information
Traditional cloud infrastructure exposes sensitive information to operators and administrators.
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Model checkpoints encode proprietary techniques
Hardware-enforced isolation prevents unauthorized access while maintaining computational efficiency.
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Gradient updates can leak training examples
End-to-end encryption protects data in transit, at rest, and critically during computation.
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Consortium partners need data-custody guarantees
Cryptographic verification ensures code integrity and proves execution in genuine TEE hardware.
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Deploy a fully optimized system and upgrade your current setup.
Secure Data Loading
Load training datasets directly from private sources inside TEEs. Your sensitive data never leaves the secure enclave during the entire training pipeline.
Encrypted Gradient Flow
Training gradients stay encrypted end-to-end. Hardware attestation proves your model updates never leaked.
Verifiable Attestation Reports
Every training run generates cryptographic proof that your data remained confidential throughout the process.
Global TEE Infrastructure
Train on distributed TEE clusters worldwide. Scale your confidential training workloads across secure data centers with hardware-level isolation.
Deploy Multi-GPU Training
Launch distributed pre-training jobs on confidential GPU clusters. Slurm and Kubernetes templates with TEE attestation and sealed checkpoint storage.
# Deploy distributed training on TEE cluster
docker run -d \
--name phala-training \
--gpus all \
--device=/dev/tdx_guest \
-v $(pwd)/data:/data \
-v $(pwd)/checkpoints:/checkpoints \
-e WORLD_SIZE=8 \
-e RANK=0 \
-e MASTER_ADDR=10.0.1.100 \
-e MASTER_PORT=29500 \
-e MODEL_CONFIG=/data/llama-70b.json \
-e TRAINING_DATA=/data/consortium/*.jsonl \
-e CHECKPOINT_DIR=/checkpoints \
phalanetwork/training:latest
# Monitor training progress
docker logs -f phala-training
# Training output from sealed environment
# Epoch 1/10: Loss 2.134 | Throughput 1.2M tok/s
# Epoch 2/10: Loss 1.876 | Throughput 1.2M tok/s
# Checkpoint saved: /checkpoints/epoch-2.bin
# Attestation signed: 0x8a9b7c6d...Verify Training Lineage
Generate cryptographic proofs of your training process. Verify cluster attestation, dataset hashes, and reproducible build IDs for auditors and consortium partners.
# Get cluster attestation and training lineage
curl -X POST https://cloud-api.phala.network/api/v1/training/verify \
-H "Content-Type: application/json" \
-d '{
"job_id": "train-consortium-llama-70b",
"verify_cluster_attestation": true,
"verify_dataset_hashes": true,
"verify_checkpoint_lineage": true
}'
# Attestation proves sealed training
{
"verified": true,
"cluster_size": 8,
"tee_type": "Intel TDX",
"dataset_hashes": [
"0x8a9b7c6d...",
"0x1a2b3c4d..."
],
"checkpoint_lineage": "llama-70b-base -> epoch-10.bin",
"reproducible_build_id": "0xfe7d8c9b...",
"timestamp": "2025-01-15T14:30:00Z"
}Industry-Leading Enterprise Compliance
Meeting the highest compliance requirements for your business
Explore Our Solutions
Discover how Phala Network enables privacy-preserving AI across different use cases
Frequently Asked Questions
Everything you need to know about Confidential Training
What's the performance overhead for multi-GPU TEE training?
GPU TEE overhead is typically 5-15% compared to bare metal. Memory encryption happens at hardware speed with Intel TDX/AMD SEV. High-speed RDMA interconnect keeps gradient synchronization efficient even across encrypted enclaves.
How does cost compare to standard cloud training?
TEE infrastructure adds 10-20% premium over standard GPU instances. However, consortium learning splits costs across partners while maintaining data custody—often more economical than each party training separately.
How do consortium partners maintain data custody?
Each partner's data stays in separate sealed storage. Training orchestrator coordinates gradient updates without exposing raw data cross-party. Remote attestation proves proper isolation before any party sends datasets.
Can cloud operators access training data or gradients?
No. Hardware memory encryption in TEEs prevents any operator access to runtime state. Gradients are computed and synchronized inside encrypted enclaves with cryptographic proofs of isolation.
What prevents gradient-based training data leakage?
Gradients are computed inside TEEs and never leave in plaintext. Differential privacy techniques can be applied within the enclave. Only final model checkpoints are exported with signed attestation lineage.
What distributed training patterns are supported?
Tensor parallelism, data parallelism, pipeline parallelism, and hybrid strategies. Phala's confidential scheduler supports PyTorch FSDP, DeepSpeed, and Megatron-LM inside TEE clusters.
Can we use existing training scripts?
Yes, minimal changes required. Wrap your training code in our confidential container and configure attestation policies. Standard frameworks (PyTorch, TensorFlow, JAX) run as-is inside TEEs.
How do we monitor training inside TEEs?
Enclave-safe telemetry exports training metrics without exposing sensitive data. TensorBoard and Weights & Biases integrations available with differential privacy filters for metric publishing.
Start Confidential Training Today
Train large-scale AI models on sensitive datasets with multi-GPU TEE clusters and hardware-enforced encryption.
Deploy on Phala- Multi-GPU TEE clusters
- Consortium learning support
- Sealed checkpoint storage
- Reproducible attestation
- 24/7 technical support
