Federated Learning with zkML

We architect your federated learning system from the ground up with zero-knowledge proofs (zk-SNARKs/STARKs) to ensure model training occurs on-device, with only verifiable updates aggregated centrally. This guarantees data never leaves its source, meeting strict compliance requirements.

Our team develops your core ML model (TensorFlow/PyTorch) and the corresponding zkML circuits (using Circom, Halo2, or Noir) to generate cryptographic proofs of correct training execution. We focus on gas-efficient circuit design for on-chain verification.

We deploy and audit smart contracts (Solidity, Rust) that verify zk proofs on-chain (EVM, SVM, or appchain). This creates a tamper-proof, trustless record of model updates, enabling decentralized consensus on the federated learning process.

We implement the secure aggregation server and client-side SDKs that coordinate the federated learning rounds, handle proof generation, and aggregate encrypted model updates without decrypting individual contributions.

We rigorously benchmark and optimize the entire pipeline—from proof generation speed and on-chain verification cost to final model accuracy—ensuring the system is production-viable and cost-effective at scale.

We manage the full deployment of your zkML federated learning network and provide ongoing monitoring, model retraining pipelines, and protocol upgrades. Includes comprehensive documentation and developer training.