README.md

Feature Election Examples

Examples demonstrating federated feature selection using NVIDIA FLARE.

Quick Start

Run the synthetic data example with auto-tuning:

python job.py --num-clients 3 --auto-tune --fs-method mutual_info

Files

File	Description
job.py	Main entry point - creates and runs FL job
client.py	Client-side executor with synthetic data loading
server.py	Server configuration helpers

Usage

Basic Run

python job.py --num-clients 3 --num-rounds 5

With Auto-tuning

python job.py --num-clients 3 --auto-tune --tuning-rounds 4

Different Feature Selection Methods

# Lasso (default)
python job.py --fs-method lasso

# Mutual Information
python job.py --fs-method mutual_info

# Random Forest
python job.py --fs-method random_forest

# Elastic Net
python job.py --fs-method elastic_net

Custom synthetic dataset configuration

python job.py \
    --n-samples 2000 \
    --n-features 200 \
    --n-informative 40 \
    --n-redundant 60 \
    --split-strategy dirichlet

Parameters

Parameter	Default	Description
--num-clients	3	Number of federated clients
--num-rounds	5	FL training rounds
--freedom-degree	0.5	Feature inclusion threshold (0-1)
--auto-tune	False	Enable freedom degree optimization
--tuning-rounds	4	Rounds for auto-tuning
--fs-method	lasso	Feature selection method
--split-strategy	stratified	Data splitting strategy
--n-samples	1000	Total synthetic samples
--n-features	100	Number of features
--workspace	/tmp/nvflare/feature_election	Simulator workspace

Customization

Using Your Own Data

Modify client.py to load your data instead of synthetic data:

class MyDataExecutor(FeatureElectionExecutor):
    def _load_data_if_needed(self, fl_ctx):
        if self._data_loaded:
            return
        
        # Load your data
        X_train, y_train = load_my_data(self.client_id)
        self.set_data(X_train, y_train)
        self._data_loaded = True

Exporting Job Configuration

python job.py --export-dir ./exported_jobs

This creates FLARE job configs that can be deployed to production.