Federated Learning Framework Enables Privacy-Preserving Training of Multimodal Models

Global: Federated Learning Framework Enables Privacy-Preserving Training of Multimodal Models

Researchers have introduced FedUMM, a federated learning framework designed for unified multimodal models (UMMs), in a preprint posted on arXiv. The system aims to address privacy and geographic distribution challenges inherent in centralized training by allowing multiple clients to collaboratively fine‑tune a shared model without exposing raw data. Leveraging NVIDIA FLARE, the framework targets scenarios where data heterogeneity and communication efficiency are critical.

Framework Overview

FedUMM builds on a BLIP‑3o backbone and employs parameter‑efficient fine‑tuning via Low‑Rank Adaptation (LoRA) adapters. Clients train only these lightweight adapters while the underlying foundation model remains frozen, and the central server aggregates solely the adapter updates. This design reduces the computational burden on edge devices and limits the amount of information exchanged during each training round.

Experimental Setup

The authors evaluated the approach on the VQA v2 visual question answering benchmark and the GenEval compositional generation suite. Experiments simulated up to 16 participating clients with non‑IID multimodal data generated through Dirichlet‑controlled heterogeneity, reflecting realistic distribution shifts across devices.

Performance Outcomes

Results indicated only slight degradation in accuracy as the number of clients and data heterogeneity increased, while overall performance remained competitive with traditional centralized training. The findings suggest that FedUMM can preserve model quality despite the constraints imposed by federated learning.

Efficiency and Trade‑offs

Analysis of computation‑communication trade‑offs demonstrated that adapter‑only federation cuts per‑round communication by more than an order of magnitude compared with full model fine‑tuning. This reduction enables more practical deployment of federated UMM training in bandwidth‑limited environments.

Overall, the study provides empirical evidence supporting the feasibility of privacy‑preserving federated training for large‑scale multimodal models and outlines avenues for future research on scalable, secure AI collaboration.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.