Federated Learning Coordination on Blockchain

Aggregate insights from millions of edge devices without centralizing sensitive data, leading to more robust and accurate AI models for predictive maintenance and user behavior analysis.

Eliminate the need for massive, centralized data lakes and the associated storage/compute costs. Process data at the edge, coordinating only model updates via blockchain.

Deploy a production-ready, privacy-preserving ML pipeline in weeks, not months. Our pre-audited smart contracts and coordination layer handle the complex orchestration.

Demonstrate GDPR/CCPA compliance with an immutable, auditable record of model training. Data never leaves the user's device, only encrypted parameter updates are shared.

Seamlessly manage and incentivize participation across heterogeneous fleets—from sensors to gateways. Automated, trustless rewards for quality data contributions.

Create new revenue streams by allowing your devices to contribute to collective intelligence. Participants earn rewards for useful updates, fostering a sustainable data ecosystem.

We design a custom federated learning coordination layer, selecting optimal consensus (e.g., proof-of-stake for privacy) and data flow protocols to match your use case's privacy, speed, and cost requirements.

Development of on-chain coordination contracts (Solidity/Rust) and off-chain client nodes. Includes incentive mechanisms, secure aggregation logic, and verifiable computation proofs.

Comprehensive security review of smart contracts and coordination logic by third-party auditors. We target critical vulnerabilities in aggregation and incentive distribution before mainnet deployment.

Deploy the full stack to a testnet (e.g., Sepolia, Hyperspace) and run simulated federated learning rounds. We validate model update integrity, network resilience, and gas cost predictability.

Managed deployment to your target mainnet (Ethereum, Polygon, etc.) with secure key management. We orchestrate the initial participant nodes and ensure the coordination layer is live and stable.

Post-launch monitoring, performance tuning, and upgrade management. We provide analytics on model convergence, participant performance, and cost efficiency, with optional retainer support.