  We consider a project (model) owner that would like to train a model by
utilizing the local private data and compute power of interested data owners,
i.e., trainers. Our goal is to design a data marketplace for such decentralized
collaborative/federated learning applications that simultaneously provides i)
proof-of-contribution based reward allocation so that the trainers are
compensated based on their contributions to the trained model; ii)
privacy-preserving decentralized model training by avoiding any data movement
from data owners; iii) robustness against malicious parties (e.g., trainers
aiming to poison the model); iv) verifiability in the sense that the integrity,
i.e., correctness, of all computations in the data market protocol including
contribution assessment and outlier detection are verifiable through
zero-knowledge proofs; and v) efficient and universal design. We propose a
blockchain-based marketplace design to achieve all five objectives mentioned
above. In our design, we utilize a distributed storage infrastructure and an
aggregator aside from the project owner and the trainers. The aggregator is a
processing node that performs certain computations, including assessing trainer
contributions, removing outliers, and updating hyper-parameters. We execute the
proposed data market through a blockchain smart contract. The deployed smart
contract ensures that the project owner cannot evade payment, and honest
trainers are rewarded based on their contributions at the end of training.
Finally, we implement the building blocks of the proposed data market and
demonstrate their applicability in practical scenarios through extensive
experiments.
