Building Federated Multimodal AI Workflows with NVIDIA FLARE | NVIDIA Technical Blog

This post details how NVIDIA FLARE orchestrates federated multimodal AI training by supporting both parameter-efficient (adapter-based) and full-model communication patterns. It addresses network and memory constraints through mechanisms like large-object externalization, tensor streaming, and disk-backed aggregation. The FedUMM example demonstrates federating lightweight adapters (LoRA) over a frozen BLIP backbone, reducing per-client communication from 28.6 GB to 0.094 GB per round while maintaining model performance near centralized baselines. The post also highlights the engineering challenges of defining client update contracts and efficient update aggregation, with FLARE's Recipe API, Tensor Downloader, and disk offload modules providing solutions.