  The difficulty in acquiring a sufficient amount of training data is a major
bottleneck for machine learning (ML) based data analytics. Recently,
commoditizing ML models has been proposed as an economical and moderate
solution to ML-oriented data acquisition. However, existing model marketplaces
assume that the broker can access data owners' private training data, which may
not be realistic in practice. In this paper, to promote trustworthy data
acquisition for ML tasks, we propose FL-Market, a locally private model
marketplace that protects privacy not only against model buyers but also
against the untrusted broker. FL-Market decouples ML from the need to centrally
gather training data on the broker's side using federated learning, an emerging
privacy-preserving ML paradigm in which data owners collaboratively train an ML
model by uploading local gradients (to be aggregated into a global gradient for
model updating). Then, FL-Market enables data owners to locally perturb their
gradients by local differential privacy and thus further prevents privacy
risks. To drive FL-Market, we propose a deep learning-empowered auction
mechanism for intelligently deciding the local gradients' perturbation levels
and an optimal aggregation mechanism for aggregating the perturbed gradients.
Our auction and aggregation mechanisms can jointly maximize the global
gradient's accuracy, which optimizes model buyers' utility. Our experiments
verify the effectiveness of the proposed mechanisms.
