Avalanche Subnet for Federated Learning

Deploy a production-ready federated learning environment in under 4 weeks. Our pre-configured subnet templates and SDKs eliminate months of infrastructure setup, letting your team focus on model architecture and data pipelines.

Implement federated learning with zero raw data exchange. Our subnet architecture ensures model training occurs on-premise at each node, with only encrypted parameter updates aggregated on-chain, meeting strict GDPR and HIPAA compliance requirements.

Eliminate cloud GPU sprawl with a purpose-built blockchain. Our subnet's fee mechanism provides predictable compute costs per training round, with transaction fees up to 90% lower than general-purpose L1s, ensuring scalable model training.

Every model update is immutably recorded on the Avalanche ledger. This creates a tamper-proof audit trail for model versions, data contributor attribution, and training consensus, essential for regulated industries and model certification.

Run your federated learning protocol on a dedicated, vertically integrated subnet. Achieve sub-2 second finality for consensus on model updates and customize VM rules (gas fees, validators) to optimize for your specific ML workloads.

Scale your federated network securely. We provide white-label node software and management dashboards, allowing data partners (hospitals, banks, devices) to join your training pool in minutes without deep blockchain expertise.

We design a custom Avalanche Subnet architecture optimized for federated learning workloads, including a purpose-built consensus mechanism for secure, private model aggregation. Includes a detailed technical specification and network topology.

Development of core smart contracts for participant onboarding, incentive distribution, and verifiable computation proofs. Built with Solidity 0.8+ and OpenZeppelin libraries, following security-first patterns.

Comprehensive security review of all smart contracts and off-chain components. We conduct internal audits and facilitate third-party reviews with leading firms like CertiK or Quantstamp before mainnet deployment.

Full deployment to the Avalanche Fuji Testnet. We execute end-to-end validation of the federated learning cycle, stress-test network performance, and simulate adversarial conditions to ensure robustness.

Managed deployment to the Avalanche Mainnet. We provision and configure validator nodes, set up monitoring and alerting (Prometheus/Grafana), and establish governance parameters for the live Subnet.

Post-launch monitoring, performance tuning, and upgrade management. We provide dedicated DevOps support, handle protocol upgrades, and offer analytics on model participation and network health.