Differentially Private Auction Design for Federated Learning With non-IID Data

Kean Ren, Guocheng Liao, Qian Ma, Xu Chen

Abstract: Federated learning (FL) is a distributed machine learning scheme in which clients jointly train a model without exposing their private data to a central server. However, two challenges exist: one technical challenge of the non-IID issue and one economic challenge of the incentive issue. Many existing works presented incentive mechanisms to select clients with high-quality data to tackle the non-IID issue. However, the existing works assumed the server's availability of clients’ true data quality information. We notice that this assumption is hard to satisfy due to the private nature of the information. In this article, we try to eliminate this assumption and adopt a local differentially private mechanism in the incentive mechanism. In this regard, we propose a Bayesian-based method for the server to estimate the clients’ qualities and an efficient algorithm that incentivizes clients with approximately high-quality data. We prove that our solution has an approximation guarantee and is incentive-compatible, individually rational, and computationally efficient. We also analyze the quality loss due to the integration of the privacy-preserving mechanism. We conduct extensive experiments and show that our proposed solution outperforms the mechanism without considering the non-IID issue and is comparable to the mechanism without privacy protection.