FlowerTune LLM on Medical Dataset

This directory conducts federated instruction tuning with a pretrained Qwen2.5-7B-Instruct model on a Medical dataset. We use Flower Datasets to download, partition and preprocess the dataset. Flower's Simulation Engine is used to simulate the LLM fine-tuning process in federated way, which allows users to perform the training on a single GPU.

PEFT Adapter

The fine-tuning results have been submitted as a PEFT adapter and can be accessed here:

• FlowerTune-Qwen2.5-7B-Instruct-Medical-PEFT

Methodology

This experiment performs federated LLM fine-tuning with LoRA using the 🤗PEFT library. The clients' models are aggregated with FedProx strategy.

Qwen2.5-7B-Instruct

For the Qwen-2.5 7B Instruct model, we adopted the following fine-tuning methodology:

• Precision: bf16 for model weights, tf32 for gradients and optimizer states.
• Quantization: 4-bit quantization for reduced memory usage.
• Optimizer: Paged AdamW 8-bit for effective optimization under constrained resources.
• LoRA Configuration:
  • Rank (r): 8
  • Alpha: 32
  • Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Training Configuration:
  • Batch size: 16
  • Maximum number of steps: 6
  • Warmup steps: 2
  • Total number of rounds: 100
  • Fraction fit per round: 0.15
• Learning Rate Scheduler: Constant learning rate scheduler with warmup steps, where:
  • Maximum LR: 5e-5
  • Minimum LR: 1e-6
• Strategy: FedProx

When bf16 and tf32 are enabled, model weights are stored in bf16 format, while gradients are computed in half-precision and converted to full 32-bit precision for updates.

Training Loss Visualization

Below is the training loss plot from the experiment:

This methodology enabled efficient fine-tuning within constrained resources while ensuring competitive performance.

Evaluation Results

• pubmedqa: 0.446
• medqa: 0.5546
• medmcqa: 0.3906
• average: 0.4637

Communication Budget

46228 Megabytes

Environments setup

Project dependencies are defined in pyproject.toml. Install them in an activated Python environment with:

pip install -e .

Experimental setup

The dataset is divided into 20 partitions in an IID fashion, a partition is assigned to each ClientApp. We randomly sample a fraction (0.15) of the total nodes to participate in each round, for a total of 100 rounds. All settings are defined in pyproject.toml.

Important

Please note that [tool.flwr.app.config.static] and options.num-supernodes under [tool.flwr.federations.local-simulation] are not allowed to be modified for fair competition if you plan to participated in the LLM leaderboard.

Running the challenge

Run the challenge with default config values. The configs are defined in [tool.flwr.app.config] entry of pyproject.toml, and are loaded automatically.

flwr run

VRAM consumption

We use Mistral-7B model with 4-bit quantization as default. The estimated VRAM consumption per client for each challenge is shown below:

Challenges	GeneralNLP	Finance	Medical	Code
VRAM	~25.50 GB	~17.30 GB	~22.80 GB	~17.40 GB

You can adjust the CPU/GPU resources you assign to each of the clients based on your device, which are specified with options.backend.client-resources.num-cpus and options.backend.client-resources.num-gpus under [tool.flwr.federations.local-simulation] entry in pyproject.toml.

Model saving

The global PEFT model checkpoints are saved every 5 rounds after aggregation on the sever side as default, which can be specified with train.save-every-round under [tool.flwr.app.config] entry in pyproject.toml.

Note

Please provide the last PEFT checkpoint if you plan to participated in the LLM leaderboard.