Federated heterogeneity refers to the disparities in data distributions, model architectures, and communication capabilities across various devices or institutional entities. In real-world scenarios, statistical heterogeneity can often lead to ineffective aggregation, severely impacting generalization performance and resulting in biased or unstable model weights. Theoretically, distributional robustness analysis indicates that the generalization performance of a learning model can be bounded with respect to any heterogeneity distribution. This insight motivates us to reconsider the aggregation strategy in federated statistical heterogeneity scenarios, and we thus propose a new weighting aggregation protocol that considers the generalization bound disagreement of each local model. Specifically, we estimate the upper and lower bounds of the second-order origin moment of the shifted distribution for the current local model, and using these bound disagreements as the aggregation proportions for weights in each communication round. Our experiments demonstrate that this proposed aggregation protocol significantly improves the performance of several representative Federated Learning algorithms on benchmark datasets.
Xu et al. (2026) studied this question.