Privacy-Preserving Incentive Mechanism for Federated Learning in Regional Carbon Allowances Management and Trading

Integrating federated learning (FL) with differential privacy (DP) into regional carbon allowances management and trading enables collaborative model training that protects both local data privacy and model security. However, the noise injection in DP degrades model accuracy, necessitating additional local training and increasing the overall computational overhead for enterprises. This overhead reduces the willingness of rational and self-interested participants to contribute to model training. To address this challenge, this study models the interaction between the regulator and enterprises as a Stackelberg game and proposes a scalable, bandwidth-efficient FL framework for regional carbon allowances management and trading. The framework adaptively determines the number of local training iterations based on enterprises’ privacy budgets and employs a submodular enterprise selection strategy for aggregation, which decreases redundant uploads and enhances global model quality. Furthermore, a deep reinforcement learning–based multi-objective differential evolution with bi-level optimization (DRL-MODE2) algorithm is developed to efficiently solve large-scale, high-dimensional Stackelberg games. Comprehensive experiments were conducted on both a carbon emission time-series dataset and the MNIST benchmark dataset under different enterprise scales, incentive mechanisms, and optimization algorithms. Experimental results demonstrate that the proposed framework incentivizes enterprise participation and improves model accuracy, without compromising privacy preservation and communication efficiency.