Federated Learning Coordination via Blockchain

Custom smart contracts for model aggregation, participant registration, and incentive distribution. Built with Solidity 0.8+ and OpenZeppelin libraries for maximum security and upgradability.

• Key Protocols: ERC-20 for incentives, custom logic for task assignment.
• Security: Formal verification and audit-ready code patterns.

Privacy-preserving aggregation logic that ensures raw training data never leaves participant nodes. We implement cryptographic techniques like Secure Multi-Party Computation (SMPC) or Homomorphic Encryption within the federated learning workflow.

• Privacy Guarantee: Data remains local; only encrypted model updates are shared.
• Flexibility: Protocol selection based on your specific privacy vs. performance requirements.

A robust, containerized middleware service that manages the federated learning lifecycle. It dispatches tasks, validates incoming model updates, and interfaces with the blockchain for on-chain settlement.

• Tech Stack: Typically built with Node.js/Python, Docker, and Kubernetes.
• Reliability: Designed for high availability and automatic failover.

Developer-friendly SDKs that allow data owners to easily join your network. Includes libraries for secure local training, model update submission, and wallet integration for incentive claims.

• Languages: Python, JavaScript/TypeScript support.
• Focus: Reduces integration time for your network participants from weeks to days.

A customizable economic system to reward high-quality data contributions and penalize malicious actors. Includes staking mechanisms, slashing conditions, and reputation scoring tracked on-chain.

• Mechanism Design: Tailored tokenomics to align participant behavior with network goals.
• Transparency: All rewards and reputations are verifiable on the public ledger.

A real-time dashboard for network operators to monitor training progress, participant performance, model accuracy, and blockchain transaction status. Provides actionable insights for network health.

• Features: Live metrics, alerting systems, and historical performance reports.
• Outcome: Enables proactive management and optimization of your federated network.

Deploy a verifiable privacy layer with on-chain attestations for each training round. This creates an immutable audit trail for GDPR, HIPAA, and CCPA compliance, turning a complex requirement into a competitive advantage.

Our coordination layer eliminates bottlenecks in peer discovery and incentive alignment. Smart contracts automate participant selection and reward distribution, reducing coordination overhead by up to 70% and accelerating time-to-accurate-model.

Replace custom-built coordination servers and complex API integrations with a unified, on-chain protocol. We handle node orchestration, state consensus, and payment settlements, allowing your team to focus solely on core AI/ML logic.

Build a transparent marketplace for trained models or data contributions. Our system provides cryptographic proof of contribution quality and origin, enabling fair, automated monetization and attracting higher-quality participants.

Every federated learning round is anchored on-chain with verifiable proofs. This provides CTOs and security teams with an immutable record for internal audits, regulatory reviews, and investor due diligence.

Launch a decentralized training network that grows organically. Our token-incentivized coordination mechanism attracts diverse data providers, increasing model robustness and accuracy without centralized recruitment costs.

Custom Solidity contracts implementing secure aggregation patterns (e.g., FedAvg, Secure Aggregation) with zero-knowledge proofs for verifiable computation. Built on OpenZeppelin standards and audited for vulnerabilities.

Decentralized coordination across Ethereum, Polygon, and Arbitrum for optimal cost and speed. Our relayers handle cross-chain model update verification, ensuring global consensus without vendor lock-in.

Immutable IPFS-based storage for model checkpoints with on-chain hashing for provenance. Every model version is cryptographically signed and traceable to its contributing nodes and training rounds.

Lightweight SDKs in Python, Go, and Rust for easy integration into existing ML pipelines. Includes secure enclave support (Intel SGX) for local data processing and automated incentive claiming.