FL-Market: Trading Private Models in Federated Learning

Federated learning (FL) is an emerging machine learning paradigm, in which data owners can collaboratively train a model without sharing their raw data. Two fundamental research problems in FL are the incentive mechanism and privacy protection. The former focuses on how to incentivize data owners to participate in FL. The latter studies how to protect data owners' privacy while maintaining high utility of trained models. However, the incentive mechanism and privacy protection in FL have been studied separately, and no work simultaneously solves both problems. In this work, we address the two problems simultaneously with FL-Market, which incentivizes data owners' participation by providing appropriate payments and privacy protection. FL-Market enables data owners to obtain compensation according to their privacy loss quantified by local differential privacy (LDP). Our insight is that by meeting data owners' personalized privacy preferences and providing appropriate payments, we can (1) incentivize privacy risk-tolerant data owners to set larger privacy parameters (i.e., gradients with less noise) and (2) provide preferred privacy protection for privacy risk-averse data owners. To achieve this, we design a personalized LDP-based FL framework with a deep learning-empowered auction mechanism to incentivize trading private models with less noise and an optimal aggregation mechanism for aggregating local gradients into an accurate global gradient. Our experiments verify the effectiveness of the proposed framework and mechanisms.