FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal
  Large Language Models
Authors: name: Yao Zhang, name: Hewei Gao, name: Haokun Chen, name: Weiguo Li, name: Yunpu Ma, name: Volker Tresp

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 deploy LLM on clients, reducing client-side storage by 95%, and
limiting communication overhead to only 0.01% of the 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.