Incentive Mechanism for Federated Learning in Data Heterogeneity and Consumer Privacy Protection

Consumer privacy protection demands are complex and multifaceted. Traditional incentive mechanisms struggle to balance participation enthusiasm with privacy risk control, leaving consumers exposed to unfair contribution evaluations and privacy leakage risks. To address this, this paper develops a collaborative joint learning incentive mechanism integrating graph neural networks (GNN) and multi-agent reinforcement learning. The approach first constructs node relationship graphs using GNN, then measures data distribution similarity through graph convolutional networks, and finally establishes a multi-agent reinforcement learning framework where nodes act as intelligent agents. By leveraging joint reinforcement learning and a dual-objective reward function, the mechanism optimizes strategies. Experimental results demonstrate that GNN-Shapley achieves over 97% accuracy, while the privacy compensation mechanism elevates average accuracy to 98.27%. This methodology effectively alleviates participation bottlenecks and safeguards consumer rights.