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.

翻译：我们考虑一个项目（模型）拥有者，希望利用感兴趣的数据拥有者（即训练者）的本地私有数据和计算能力来训练模型。我们的目标是为此类去中心化协作/联邦学习应用设计一个数据市场，该市场同时具备以下特性：i) 基于贡献证明的奖励分配机制，使训练者根据其对训练模型的贡献获得相应报酬；ii) 隐私保护的去中心化模型训练，避免数据拥有者的任何数据迁移；iii) 抵御恶意参与方（例如旨在投毒模型的训练者）的鲁棒性；iv) 可验证性，即数据市场协议中所有计算（包括贡献评估和异常值检测）的完整性（正确性）均可通过零知识证明进行验证；v) 高效且通用的设计方案。我们提出一个基于区块链的市场设计，以实现上述全部五个目标。在该设计中，除项目拥有者和训练者外，我们还利用分布式存储基础设施和一个聚合器。聚合器是执行特定计算（包括评估训练者贡献、剔除异常值和更新超参数）的处理节点。我们通过区块链智能合约来执行所提出的数据市场。部署的智能合约可确保项目拥有者无法逃避支付，诚实训练者在训练结束时能根据其贡献获得奖励。最后，我们实现了所提出数据市场的各构建模块，并通过大量实验展示了其在实际场景中的适用性。