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.
翻译:获取足够数量的培训数据的困难是机器学习(ML)基于数据分析分析的主要瓶颈。最近,有人提议将ML模型进行商品化,作为以ML为导向的数据获取的经济和中度解决办法;然而,现有的示范市场假设经纪人可以访问数据所有人私人培训数据,而实际上可能并不现实。在本文件中,为了促进为ML任务获取可信赖的数据,我们提议FL-Martet,这是一个本地私营的模型市场,不仅保护隐私不受模型买主的干扰,而且防止不受信任的经纪人的干扰。FL-市场拆解 ML,从需要的角度出发,用FL-Market,集中收集经纪人一方的培训数据,这是以FL-ML为导向的数据获取中一种新兴的保密ML模式,数据所有者通过上传本地梯度来合作培训ML模型(以汇总为模型更新的全球梯度)。随后,FL-Market使数据所有者能够通过本地差异隐私和进一步防止隐私风险,从而集中收集经纪人方方面的培训数据数据数据。我们提议,一个深学习-动力化全球增值的升级机制,用以决定我们地方最大程度的升级的升级机制。