Enabling verifiability in federated learning utilizing zero-knowledge proofs and blockchain

Abstract

To address the absence of process-level verifiability in federated learning, a verifiable architecture, zero-knowledge proof-verified and blockchain-audited federated learning (zk-BcFed), is proposed by integrating zero-knowledge proofs with blockchain. For each local model update, a multi-constraint zero-knowledge proof is generated by the client, and verified cryptographic evidence is recorded on-chain, enabling formal verification of local training without disclosure of private data. Across benchmark datasets including SVHN, FashionMNIST, and CIFAR10, among others, enabling zero-knowledge proofs is observed to produce a negligible change in accuracy while substantially improving robustness under model poisoning attacks. Collectively, zk-BcFed safeguards the computational integrity and correctness of federated learning and provides a reliable verifiability mechanism with modest overhead.