Calibration-Safe Heterogeneous-Rank Federated LoRA: A Multi-Backbone Equivalence Study with a Lossless SVD-Merge Aggregator

We have three main contributions to this paper. First, we have analyzed not only the accuracy but also the calibration in the case of heterogeneous-rank federated LoRA aggregation for the first time. Second, we have adapted the FlexLoRA materialize–average–recompress recipe into SVD-Merge, a lossless aggregator that we have used as a faithful-aggregation baseline for the calibration study. Third, we have presented a statistically robust equivalence result.