Decentralized IoT data sharing: A blockchain-based federated learning approach with joint optimizations for efficiency and privacy

Abstract

Blockchain-based Federated Learning (BCFL) is gaining significant attention as a promising decentralized data sharing technology with privacy protection. Most existing BCFL frameworks loosely couple blockchain and Federated Learning (FL). FL transforms data sharing into model sharing, while blockchain decentralizes the model aggregation and handles security verification. However, this simplistic overlay of the two technologies often involves third-party blockchain peers, leading to inefficient workflows and potential privacy attacks from malicious blockchain peers. Some studies have proposed differential privacy with noise addition to prevent privacy attacks, yet most do not allow clients to customize privacy budgets, impacting data availability and limiting BCFL’s application in data sharing. To address these challenges, we first introduce a novel tightly-coupled BCFL framework that integrates training and mining at the client side. Under this framework, a totally decentralized data sharing process is established. Moreover, a Personalised Differential Privacy (PDP) mechanism is devised, enabling clients to add Laplace noise to model gradients based on custom privacy budgets. To achieve optimal privacy budgets, a privacy optimization mechanism based on Stackelberg games is proposed. It establishes utility functions for data requesters and providers, models the process of utility optimization as a Stackelberg game process, and obtains the optimal privacy budget while maximizing utility for all participants. This facilitates flexible and on-demand privacy-protected data sharing. Extensive experiments validate the effectiveness of our approach in enhancing system efficiency and facilitating flexible on-demand privacy-protected data sharing, further solidifying BCFL’s potential in decentralized data sharing scenarios.