Federated Learning Method Based on Data Distribution Heterogeneity Grading and Marginal Contribution Calculation

As federated learning scales up in distributed scenarios, training instability and performance degradation caused by data quality issues—such as statistical heterogeneity and noise—have become major bottlenecks for practical deployment. Existing aggregation algorithms have been shown to not adequately account for differences in data importance. This can exacerbate client selection bias and incentive misalignment. As a result, global convergence can slow down and performance can deteriorate. To address this issue, this paper proposes a robust federated learning framework based on data heterogeneity grading and marginal contribution calculation. The objective of this study is to enhance the overall performance of federated learning systems in heterogeneous environments by quantifying data importance. The framework first grades and quantifies the heterogeneity of client data distributions, precisely characterizing data importance while reducing the computational space for Shapley value calculations, effectively lowering its exponential complexity. Subsequently, it integrates client marginal contributions with data distribution heterogeneity to establish a dynamic weighted aggregation mechanism that balances fairness, robustness, and differentiated data quality requirements. Multi-dataset comparative experiments demonstrate that the proposed method achieves consistent gains in model accuracy and convergence under non-IID splits and noisy-label settings.