A Step Toward Federated Pretraining of Multimodal Large Language Models
Summary: arXiv:2603.26786v1 Announce Type: cross
The rapid evolution of Multimodal Large Language Models (MLLMs) has been significantly hampered by the saturation of high-quality public data. Despite the existence of vast amounts of diverse multimodal data, much of it remains locked away in privacy-sensitive silos. Federated Learning (FL) emerges as a promising solution, allowing researchers to leverage these distributed resources without compromising privacy. However, existing research predominantly focuses on fine-tuning these models, leaving the foundational pre-training phase largely unexplored.
In a groundbreaking paper, researchers formally introduce the Federated MLLM Alignment (Fed-MA) task, which outlines a lightweight pre-training paradigm. This approach involves freezing the vision encoder and the language model (LLM) while collaboratively training the cross-modal projector. This innovative method aims to address the critical challenges that arise during the pre-training process.
Challenges in Federated Pre-training
Two main challenges have been identified in the federated pre-training setting:
- Parameter Interference: When aggregating local projectors, the interference among parameters can lead to suboptimal model performance.
- Gradient Oscillations: In one-pass collaborative Stochastic Gradient Descent (SGD), gradient oscillations can hinder effective learning.
Introducing Fed-CMP Framework
To tackle these challenges, the paper proposes Fed-CMP, a pioneering framework for federated MLLM pre-training. The Fed-CMP framework employs two key strategies to enhance performance:
- Canonical Reliability-Aware Aggregation: This technique constructs a canonical space to decompose client projectors into a shared alignment basis and client-specific coefficients. By performing reliability-weighted fusion, Fed-CMP effectively suppresses parameter interference, allowing for a more coherent model training process.
- Orthogonality-Preserved Momentum: This innovative approach applies momentum to the shared alignment basis via orthogonal projection. By accumulating historical optimization directions while preserving geometric structure, Fed-CMP ensures that the learning process remains stable and effective.
Experimental Validation
The researchers constructed four federated pre-training scenarios based on public datasets, allowing for extensive experimental validation of the Fed-CMP framework. The results demonstrate that Fed-CMP significantly outperforms existing baselines, confirming its efficacy as a robust solution for federated pre-training of MLLMs.
Conclusion
As the field of multimodal large language models continues to evolve, the introduction of federated pre-training represents a significant step forward. By unlocking the potential of privacy-sensitive data through innovative frameworks like Fed-CMP, researchers can pave the way for the next generation of advanced multimodal capabilities. The ongoing exploration of federated learning methodologies will be essential in overcoming existing limitations and expanding the horizons of artificial intelligence.