---

name: "extension" description: "Routes PFLlib code changes that add or update algorithms, models, optimizers, and dataset generators." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Extension

Use this route when you need to change PFLlib itself rather than run an existing benchmark.

Use this when

• You want to add a new federated-learning algorithm, client, or server.
• You want to add a new dataset generator or preprocessing flow.
• You want to add a new model, optimizer, or registry entry.
• You need to verify that a new code path is wired into main.py correctly.

Read these references

• references/api-reference.md for the Server/Client hooks and registry expectations.
• references/workflows.md for the step-by-step add-a-feature checklists.
• references/troubleshooting.md for registration, shape, and dependency mistakes.

Run these helpers

• scripts/scan_registry.py from the root route to inspect the current dataset, model, and algorithm registry before you edit it.
• scripts/check_install.py from the root route if the new feature introduces a dependency like cvxpy, torchtext, or torchvision.

What belongs here

Include code changes and documentation for:

• new server*.py / client*.py pairs
• new dataset generators under dataset/
• new model classes under the model registry
• new optimizer hooks
• registry edits in system/main.py
• any support code required to make the new feature selectable from the CLI

What does not belong here

• Launching an already-registered experiment; route that to experiments.
• Generating or validating client-split datasets that already exist; route that to data-preparation.
• Long result-analysis tasks after the change is already wired; route that back to experiments.

Common workflow

1. Inspect the current registry and the relevant base classes.
2. Implement the new code path.
3. Wire it into main.py or the dataset loader as needed.
4. Re-check the registry and run a tiny smoke path from the experiments route.
5. Only then consider larger validation or downstream benchmarking.