Flower

1. Each hospital trains the model on its local patient data.

2. Flower aggregates the model updates from each hospital.

3. The aggregated model is distributed back to the hospitals for further refinement.

4. The process is repeated until the model reaches the desired performance.

5. The final model is used for diagnosis in each hospital.

1. Each bank trains the model on its local transaction data.

2. Flower aggregates the model updates from each bank.

3. The aggregated model is distributed back to the banks for further refinement.

4. The process is repeated until the model reaches the desired performance.

5. The final model is used for fraud detection in each bank.

1. Each vehicle trains the model on its local driving data.

2. Flower aggregates the model updates from each vehicle.

3. The aggregated model is distributed back to the vehicles for further refinement.

4. The process is repeated until the model reaches the desired performance.

5. The final model is used for autonomous driving in each vehicle.

1. Each building trains the model on its local sensor data.

2. Flower aggregates the model updates from each building.

3. The aggregated model is distributed back to the buildings for further refinement.

4. The process is repeated until the model reaches the desired performance.

5. The final model is used for energy optimization in each building.

1. Select a pre-trained LLM.

2. Define a specific NLP task and dataset.

3. Use FlowerTune to set up the federated learning environment.

4. Train the LLM on the local datasets using federated learning.

5. Evaluate the performance of the fine-tuned model.