  Federated learning may be subject to both global aggregation attacks and
distributed poisoning attacks. Blockchain technology along with incentive and
penalty mechanisms have been suggested to counter these. In this paper, we
explore verifiable off-chain computations using zero-knowledge proofs as an
alternative to incentive and penalty mechanisms in blockchain-based federated
learning. In our solution, learning nodes, in addition to their computational
duties, act as off-chain provers submitting proofs to attest computational
correctness of parameters that can be verified on the blockchain. We
demonstrate and evaluate our solution through a health monitoring use case and
proof-of-concept implementation leveraging the ZoKrates language and tools for
smart contract-based on-chain model management. Our research introduces
verifiability of correctness of learning processes, thus advancing
blockchain-based federated learning.
