Pain-FL: Personalized Privacy-Preserving Incentive for Federated Learning

Federated learning (FL) is a privacy-preserving distributed machine learning framework, which involves training statistical models over a number of mobile users (i.e., workers) while keeping data localized. However, recent works have demonstrated that workers engaged in FL are still susceptible to advanced inference attacks when sharing model updates or gradients, which would discourage them from participating. Most of the existing incentive mechanisms for FL mainly account for workers’ resource cost, while the cost incurred by potential privacy leakage resulting from inference attacks has rarely been incorporated. To address these issues, in this paper, we propose a contract-based personalized privacy-preserving incentive for FL, named Pain-FL, to provide customized payments for workers with different privacy preferences as compensation for privacy leakage cost while ensuring satisfactory convergence performance of FL models. The core idea of Pain-FL is that each worker agrees on a customized contract, which specifies a kind of privacy-preserving level (PPL) and the corresponding payment, with the server in each round of FL. Then, the worker perturbs her calculated stochastic gradients to be uploaded with that PPL in exchange for that payment. In particular, we respectively derive a set of optimal contracts analytically under both complete and incomplete information models, which could optimize the convergence performance of the finally learned global model, while bearing some desired economic properties, i.e., budget feasibility, individual rationality, and incentive compatibility. An exhaustive experimental evaluation of Pain-FL is conducted, and the results corroborate its practicability and effectiveness.

I. Introduction

With the proliferation of sensor-rich mobile devices (e.g., smartphones), Internet of Things (IoT) end nodes (e.g., RFID tags), and the ubiquitous deployment of wireless communication infrastructures, an unprecedented amount of data has been generated at the network edge [1]–[3]. Collecting and mining this massive data could help build various machine learning models that can empower a wide range of intelligent applications, such as smart cities and homes [4]. The conventional machine learning paradigm requires centralizing data from data owners (e.g., mobile users), which raises serious privacy concerns [5].