Trading Trust for Privacy: Socially-Motivated Personalized Privacy-Preserving Collaborative Learning in IoT

Nowadays, collaborative federated learning (CFL) is developing rapidly in the Internet of Things (IoT), which allows clients to jointly train models without compromising private data. The existing research has studied alone either trust enhancement or privacy preservation issues in CFL. Due to the highly coupled nature of trust and privacy in a collaborative environment, it is worth investigating how to balance appropriate trust and privacy tradeoffs for realizing high-quality CFL. In this paper, we come up with the idea of "trading Trust for Privacy", and propose a novel socially-motivated personalized privacy-preserving federated learning (SP-PFL) framework, which aims to realize social trust-grained privacy protection. First, we design a social trust evaluation method among CFL clients, which is based on topological relation and attribute similarity. Based on the obtained trust value, we then propose a trust-grained privacy budget allocation strategy for SP-PFL, which could further adaptively adjust the differential privacy (DP) noise perturbation. Besides, we provide an analysis of privacy and convergence for our SP-PFL. Finally, we experiment with different models and parameter settings on different datasets. Extensive experimental results show that our method maintains personalized privacy and effectively improves the accuracy by 6.11% on the CNN model and MNIST dataset.