✅ What is FedOps VLM Framework??
FedOps VLM Framework is an open, cloud-native federated learning (FL) platform purpose-built for Vision-Language Models (VLMs). It bridges the gap between large-scale multimodal AI and privacy-preserving distributed training — enabling researchers and practitioners to fine-tune powerful VLMs across decentralized edge devices without ever centralizing raw data.
Built on top of the FedOps ecosystem (PyPI fedops), the framework combines:
- Flower — for robust FL communication and client–server orchestration
- PEFT / LoRA / QLoRA — for parameter-efficient fine-tuning of large VLMs on resource-constrained devices
- FedMAP (Parameter-Aware Federated Aggregation) — a novel component-aware weighted aggregation theorem that achieves tighter convergence bounds than flat averaging when LoRA and projection parameters have different learning dynamics.
The result is a full-stack FL solution that works from a web portal download all the way to a Kubernetes-managed cloud FL server — reducing the barrier to federated VLM research to just a few configuration steps.
Core Concepts
Federated Learning for VLMs
Traditional VLM fine-tuning requires pooling sensitive data into a central server. FedOps VLM Framework flips this: the data never leaves the device. Only lightweight parameter updates are sent to the server for aggregation.
Plugin-Based Architecture
Plugin-based — models, datasets, and aggregation strategies are swappable. Pick your combination from the web portal, download, and train. No boilerplate wiring required.
Supported Models
| Model | Size | Quantization |
|---|---|---|
| OneVision | 0.5B | QLoRA (4-bit) |
| PhiVA | 3.8B | QLoRA (4-bit) |
Custom models via
models.py
Supported Datasets
| Dataset | Domain | Size |
|---|---|---|
| VQA-RAD | Medical radiology | 1793 train, 451 test |
| VQAv2 | General vision | Large-scale |
| PathVQA | Pathology | ~19.7K train |
| SLAKE | Multi-organ medical | ~4.9K train, 1K test |
| ChartQA | Chart reasoning | ~18K train |
| DocVQA | Document understanding | ~10K train |
Custom datasets via
data_preparation.py
Aggregation Strategies
| Strategy | Description |
|---|---|
| FedAvg | Standard weighted average |
| FedMAP | Component-aware QP — separate λ for LoRA vs. projection layers |