Domain-Adaptive Model Merging Across Disconnected Modes
Learning across domains is challenging when data cannot be centralized due to privacy or heterogeneity, which limits the ability to train a single comprehensive model. Model merging provides an appealing alternative by consolidating knowledge from multiple specialized models into one, avoiding data sharing and reducing retraining cost. In this work, we present DMM, a data-free model merging framework designed to handle highly divergent models. DMM proceeds in three steps. First, domain-specific models are trained independently. Second, models with high similarity are merged using standard techniques to ensure stability. Third, we synthesize pseudo-data from normalization statistics and di...
| Authors | Junming Liu, Yusen Zhang, Rongchao Zhang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 6 March 2026 |
| Last updated at source | 15 April 2026 |
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