Title: FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: federated learning, multimodal learning, vision question answering, multimodal QA

Abstract: Multimodal Large Language Models (MLLMs) excel in tasks like multimodal reasoning and cross-modal retrieval but face deployment challenges in real-world scenarios due to distributed multimodal data and strict privacy requirements. Federated Learning (FL) offers a solution by enabling collaborative model training without centralizing data. However, realizing FL for MLLMs presents significant challenges, including high computational demands, limited client capacity, substantial communication costs, and heterogeneous client data. Existing FL methods assume client-side deployment of full models, an assumption that breaks down for large-scale MLLMs due to their massive size and communication demands. To address these limitations, we propose **FedNano**, the first FL framework that centralizes the LLM on the server while introducing NanoEdge, a lightweight module for client-specific adaptation. NanoEdge employs modality-specific encoders, connectors, and trainable NanoAdapters with low-rank adaptation. This design eliminates the need to access or modify the LLM on clients, reducing client-side storage by **95%** and communication overhead to just **0.01%** of model parameters. By transmitting only compact NanoAdapter updates, FedNano handles heterogeneous client data and resource constraints while preserving privacy. Experiments demonstrate that FedNano outperforms prior FL baselines, bridging the gap between MLLM scale and FL feasibility, and enabling scalable, decentralized multimodal AI systems.

TL;DR: FedNano centralizes the LLM on the server while using lightweight client-side adapters, achieving superior performance and efficiency for federated multimodal learning.