WTDP-Shapley: Efficient and Effective Incentive Mechanism in Federated Learning for Intelligent Safety Inspection

Abstract

Artificial Intelligence (AI) has been widely applied for Safety Inspection in a number of industrial domains. However, individual company usually could not provide sufficient data to support well-trained AI models. Federated Learning, as a new AI paradigm, enables a number of participants to contribute training data to co-create high-performance models without compromising data privacy. However, an effective incentive mechanism is essential to encourage participants to contribute high-quality data, and the fundamental of the incentive mechanism is to evaluate participants' contribution fairly. Shapley Value (SV) is a well-known approach to evaluate individual's marginal contribution in a coalition, but the canonical SV calculation and its available variants are very costly. In this paper, we proposed an FL framework to enable a number of natural gas companies from different cities to jointly train an object detection computer vision deep learning model for the purpose of identifying potential hazards, without sharing their confidential inspection photos directly. We improve state-of-the-art SV algorithm by proposing Weighted Truncation (WT) for unnecessary computations, and go further with Dynamic Programming (WTDP), achieving better trade-off between efficiency and accuracy. Based on our proposed WTDP-Shapley participant contribution evaluation approach, an effective end-to-end incentive mechanism is designed by leveraging knowledge of both data scientists and domain experts. According to our experiments, it could encourage participants to contribute scarce photos with potential hazards, and thus co-create a high-performance AI model to identify various hazards accurately for residential natural gas installation safety inspection.