PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated Multimodal LLMs

Jul 15, 2026

A new framework called PFAdapter introduces hierarchical LoRA decomposition to separate global-shared and local-private parameters for federated fine-tuning of multimodal large language models (MLLMs). By synchronizing only the global-shared components and keeping local adaptations private, PFAdapter reduces communication costs by nearly 50% and achieves accuracy improvements of 2.4% to 4.8% on several medical and multimodal datasets. The approach also uses orthogonality regularization to enforce strict separation between parameter types, preventing redundant feature learning.

Why it matters: This work offers a practical advance for deploying personalized, communication-efficient AI at network edges, addressing key challenges in federated learning for multimodal models.

Full story at: arXiv Machine Learning ↗