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Fine-tuningFreeOpen Source
FLOWER
Federated learning framework for privacy-preserving distributed AI
Apache-2.0
ABOUT
Training ML models on sensitive data (health records, financial transactions, personal messages) requires keeping data on-device or on-premises, but traditional training centralizes everything. Flower enables federated learning: models are trained across distributed clients — phones, hospitals, factories — and only weight updates, never raw data, leave each client. Flower supports simulation for rapid prototyping and production deployment with heterogeneous hardware, straggler handling, and secure aggregation. It integrates with PyTorch, TensorFlow, Hugging Face, and JAX out of the box.
INSTALL
pip install flwrINTEGRATION GUIDE
1. Train a medical image classifier across multiple hospitals without sharing patient data
2. Fine-tune an LLM on users' mobile typing data while keeping keystrokes private
3. Simulate a 1000-client federated learning experiment on a single machine before production rollout
4. Build a keyboard prediction model that improves on-device without uploading user text
5. Aggregate model updates from factory sensors across manufacturing sites to train a predictive maintenance model
TAGS
federated-learningdistributed-trainingprivacypythonmachine-learningcollaborativeedge