Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models
Vision-Language Models (VLMs) have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints render centralized training infeasible. Federated Learning mitigates this issue by enabling decentralized training, but practical deployments face challenges due to client heterogeneity in computational resources, application requirements, and model architectures. Under extreme model and data heterogeneity, replacing parameter aggregation with preference-based collaboration offers a more suitable interface, as it eliminates the need for direct parameter or data exchange. Motivated by this, we propose MoR, a federated alignment framework that c...
| Authors | Shule Lu, Yujing Wang, Hainan Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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