A Socially Optimal Data Marketplace With Differentially Private Federated Learning

Federated learning (FL) enables multiple data owners to collaboratively train machine learning (ML) models for different model requesters while keeping data localized. Thus, FL can mitigate privacy leakage in conventional data marketplaces for ML applications requiring raw data trading for centralized model training. Nevertheless, data owners involved in FL may still suffer potential privacy leakage from gradient exposure to the model requesters. In this work, we advocate a novel data marketplace with differentially private federated learning (DPFL) to reduce such threats and maximize the social welfare. Designing such a marketplace involves several challenges. First, it is difficult to determine the privacy budget that a data owner should choose for a model requester, since they have conflicting objectives and private utility/cost information. Second, each data owner sustains privacy costs from his friends' participation in DPFL due to data correlations, which introduces a negative externality to the market. We design a social-aware iterative double auction (SARDA) mechanism to resolve these challenges and achieve socially optimal market operation. SARDA employs a broker to coordinate the interactions between data owners and model requesters and induces them to truthfully report by iteratively updating the allocation and pricing rules. Moreover, SARDA accounts for the negative externality by incorporating others' bids to reimburse each data owner. We show that SARDA achieves the optimal social performance and creates up to $60\%$ higher social welfare than the social-agnostic benchmark.