Collaborative Learning
Train Together, Keep Data Private
Federated learning enables training AI models across the network without sharing raw data. Differential privacy, secure aggregation, and TEE attestation ensure cryptographic privacy guarantees.
How It Works
Four key roles work together to enable private collaborative training
Coordinators
Initiate training rounds and manage the collaborative learning process
Key Responsibilities
- •Announce training rounds
- •Set privacy parameters
- •Distribute rewards
- •Monitor progress
Contributors
Train models locally on private data and submit encrypted updates
Key Responsibilities
- •Download global model
- •Train on local data
- •Apply differential privacy
- •Submit encrypted gradients
Aggregators
Combine encrypted updates in TEEs without seeing individual contributions
Key Responsibilities
- •Secure aggregation
- •Compute global updates
- •Publish new model
- •Generate attestations
Verifiers
Validate contributions and ensure protocol compliance
Key Responsibilities
- •Check attestations
- •Verify contributions
- •Monitor privacy budgets
- •Report violations
Privacy Guarantees
Multi-layered privacy protection with cryptographic proofs
Rényi Differential Privacy
Advanced differential privacy with tighter composition bounds across multiple training rounds
Configurable epsilon (ε ∈ {0.01, 0.1, 0.5, 1, 10}) with automatic noise calibration
Secure Aggregation
Coordinator never sees individual updates, only the aggregated result computed in TEE
Cryptographic protocols ensure privacy even with malicious aggregators
TEE Attestation
Hardware-backed proof that aggregation happened correctly without data exposure
Intel SGX, AMD SEV-SNP, and ARM TrustZone attestations supported
No Raw Data Transmission
Only encrypted gradient updates leave devices, raw data stays completely private
Multi-layer encryption with end-to-end verification
Benefits of Collaborative Learning
Privacy Preservation
Train AI together without sharing sensitive data. Perfect for healthcare, finance, and regulated industries.
Collaborative Intelligence
Improve models using data from multiple sources without centralization or data pooling.
Compliance Ready
Meet GDPR, HIPAA, and other regulations through cryptographically proven privacy guarantees.
Quality Incentives
Contributors earn rewards based on the quality and quantity of their contributions.
Continuous Improvement
Models get better over time as more contributors participate in training rounds.
Decentralized Learning
No single entity controls the training process. Democratic and transparent collaboration.
Start Collaborative Training
Train AI models together while keeping data completely private