Decentralized Training
Distributed model training using edge devices without centralized data aggregation.
Federated Learning
Distributed AI Platform
A decentralized AI infrastructure that enables trustless machine learning across edge and blockchain networks.
Key Features
On-Chain Verification
Immutable model performance tracking using blockchain timestamps and proofs.
Proof of Accuracy
Edge-to-edge Inference
Real-time AI processing on resource-constrained devices using compact model representations.
Model Compression
Quantization
Technical Architecture
The platform combines cryptographic proof systems with machine learning to ensure data privacy and model integrity across devices.
Model Training Flow
- 1Data remains private on edge devices
- 2Model weights update through secure aggregation
- 3On-chain verification of training progress
- 4Immutable audit trail of model improvements
Inference Process
- 1Requests routed to available edge nodes
- 2Model computations with input encryption
- 3Results validated through cryptographic proofs
- 4Guaranteed latency under 250ms for 99% of queries
Real-World Applications
Healthcare Diagnostics
Enables medical AI research while preserving patient privacy through federated learning on hospital devices.
Smart Grid Optimization
Predictive energy load balancing using distributed models across renewable energy sources.
Join the AI Network
Participate in AI model development and validation, or contribute compute resources to the distributed network.