flwrCrate

Capture a Flower federated learning run and emit an RO-Crate describing it — a machine-readable, FAIR provenance record of who ran what, with which software, configuration, and data flow, and what came out.

The run is modelled as a Process Run Crate-style CreateAction, following the Federated Learning RO-Crate profile:

• instrument — the software that did the work: Flower, the ML framework(s) (with declared and installed versions), and the aggregation strategy (FedAvg, FedProx, …) with its hyperparameters
• object — the run configuration as PropertyValue inputs
• result — the final aggregated model and a per-round metrics / federation log file
• agent / author / license — provenance: a Person entity (ORCID used as its @id when provided), a root license, and the agent on the action

Final performance metrics are attached to the output model as schema.org PropertyValues, optionally with semantic identifiers via a user-declared metric→URI mapping.

Requirements

⚠️ flwrCrate supports Flower's message-based ServerApp API only (Flower ≥ 1.29: flwr.serverapp, @app.main(), and strategy.start(...) returning a Result).

Apps written against the classic API (server_fn, ServerAppComponents, flwr.server.strategy) are not supported.

Check an app's compatibility in seconds:

grep -rn "flwr.serverapp\|strategy.start\|server_fn\|ServerAppComponents" <app>/<pkg>/server_app.py

• flwr.serverapp and strategy.start → ✅ compatible
• server_fn or ServerAppComponents → ❌ classic API, not supported (yet)

Other requirements: Python ≥ 3.9, rocrate ≥ 0.13 (tomli on Python < 3.11).

Install

git clone https://github.com/faizollah/flwrCrate.git
pip install -e flwrCrate

Install it into the same environment that runs your Flower app (the one flwr run uses).

Usage

Integration is three touchpoints in your server_app.py: a context manager, one wrap_evaluate(...), and one record_result(...).

Before — a stock flwr new PyTorch app

@app.main()
def main(grid: Grid, context: Context) -> None:
    ...
    strategy = FedAvg(fraction_evaluate=fraction_evaluate)

    result = strategy.start(
        grid=grid,
        initial_arrays=arrays,
        train_config=ConfigRecord({"lr": lr}),
        num_rounds=num_rounds,
        evaluate_fn=global_evaluate,
    )

    if context.run_config["save-model"]:
        torch.save(result.arrays.to_torch_state_dict(), "final_model.pt")

After — with flwrCrate

from flwrcrate import FLCrateTracker

@app.main()
def main(grid: Grid, context: Context) -> None:
    ...
    strategy = FedAvg(fraction_evaluate=fraction_evaluate)

    with FLCrateTracker(
        context, strategy,
        output_dir="/absolute/path/to/your-app/fl_crate_out",        # absolute!
        pyproject_path="/absolute/path/to/your-app/pyproject.toml",  # absolute!
        app_name="My federated run",
        author={"name": "Your Name", "orcid": "https://orcid.org/0000-0000-0000-0000"},
        license="https://spdx.org/licenses/MIT.html",
    ) as tracker:
        result = strategy.start(
            grid=grid,
            initial_arrays=arrays,
            train_config=ConfigRecord({"lr": lr}),
            num_rounds=num_rounds,
            evaluate_fn=tracker.wrap_evaluate(global_evaluate),  # or None
        )
        torch.save(result.arrays.to_torch_state_dict(), "final_model.pt")
        tracker.record_result(result, model_path="final_model.pt")
    # On clean exit the RO-Crate is written to <output_dir>/ro-crate/

That is the whole integration. On exit — including on failure — the crate is built; a failed run is recorded with FailedActionStatus and the error message, so partial runs are still documented.

Apps without a server-side evaluate function

If your app passes no evaluate_fn to strategy.start(...) (common for client-side-only evaluation), skip wrap_evaluate entirely. Per-round metrics are then read from the Result object's client-side aggregates (train_metrics_clientapp / evaluate_metrics_clientapp) by record_result(...):

    with FLCrateTracker(context, strategy, ...) as tracker:
        result = strategy.start(grid=grid, initial_arrays=arrays,
                                train_config=..., num_rounds=...)
        tracker.record_result(result)   # don't forget this line!

Don't forget record_result(result). Without it the crate is still written, but it will contain no performance metrics, no end time, and no model — only the static capture from the start of the run.

Why absolute paths?

flwr run installs your app to ~/.flwr/apps/<hash>/ and executes the ServerApp from there (in simulation, inside a Ray worker that does not inherit your shell environment). Relative paths therefore resolve against the installed copy, not your project: a relative output_dir "loses" the crate in ~/.flwr/apps/..., and a relative pyproject_path fails to find your config — silently dropping the framework/dependency capture. Always pass both as absolute paths.

Configuration

FLCrateTracker(...) parameters

Parameter	Required	Description
context	yes	The Flower Context (gives access to run_config)
strategy	yes	Your strategy instance (FedAvg, FedProx, …) — class, module, and hyperparameters are captured
output_dir	recommended	Where to write outputs. Use an absolute path. Default: ./fl_crate_out
pyproject_path	recommended	Path to your app's pyproject.toml. Use an absolute path. Default: "pyproject.toml"
app_name	no	Human-readable name for the crate's root dataset
author	no	"Name" or {"name": ..., "orcid": ..., "affiliation": ...} — becomes a Person entity (ORCID as @id)
license	no	License for the crate root, e.g. an SPDX URL "https://spdx.org/licenses/MIT.html"
agent	no	Who executed the run, same format as author. Defaults to the author

Semantic metric identifiers

Metric names are taken verbatim from your MetricRecords, so any analysis (classification, regression, anomaly detection, …) works without hardcoded names. To attach semantic identifiers, declare a mapping in your app's pyproject.toml:

[tool.flwrcrate.metric-uris]
accuracy = "https://schema.org/Accuracy"
rmse     = "http://www.wikidata.org/entity/Q1374913"

Mapped metrics get a propertyID; unmapped metrics still emit a plain PropertyValue and log a warning.

Output

<output_dir>/
├── captured_metadata.json   # full capture: config, frameworks, strategy, timing, final metrics
├── metrics_log.json         # per-round metrics + federation details (referenced by the crate)
└── ro-crate/
    ├── ro-crate-metadata.json
    ├── final_model.pt        # if a model_path was given
    └── metrics_log.json

What's inside ro-crate-metadata.json

The crate's @graph contains, linked together:

Entity	Type	Content
./	Dataset	Root: name, author, license, conformsTo the FL profile, mentions the run
#fl-run	CreateAction	The run: agent, startTime/endTime, actionStatus, instrument/object/result
#flower	SoftwareApplication	Flower with its installed version
#framework-*	SoftwareApplication	Every declared dependency (minus an infrastructure deny-list): softwareRequirements = the declared version spec, softwareVersion = the actually-installed version
#fl-strategy	SoftwareApplication	The aggregation strategy with its hyperparameters as PropertyValues
#param-*	PropertyValue	Run configuration inputs (the action's object)
#metric-*	PropertyValue	Final-round metrics, attached to the output model (with propertyID when mapped)
final_model.pt, metrics_log.json	File	The run's outputs (the action's result)

Per-round metric history and federation details (participant counts, federation options) live in metrics_log.json, which the crate references as a run output — keeping the metadata file lean while preserving the full time series.

Validation

Crates validate against the official rocrate-validator. Note: ro-crate-py ≥ 0.15 writes RO-Crate 1.2, while rocrate-validator 0.10 ships only a 1.1 profile, producing a single false-positive MUST 5.3 (conformsTo version). All other REQUIRED checks pass.

Tested with

App	Strategy	Stack	Notes
@flwrlabs/quickstart-pytorch	FedAvg	PyTorch, TorchVision	server- and client-side metrics
@flwrlabs/quickstart-sklearn	FedAvg	scikit-learn	no server-side evaluate fn
@chongshenng/fed-engines	FedProx	PyTorch, HF datasets	anomaly-detection metrics (balanced accuracy, per-class recall), client-side only

👉 See a real generated crate before you run anything: examples/quickstart-pytorch/ contains the integrated server_app.py and the ro-crate-metadata.json it produced.

Known limitations / roadmap

• Classic API not supported. Apps on server_fn/ServerAppComponents (still common in published Flower apps) need porting to the message API first. Supporting the classic API is an open design question.
• Participant count under recent Flower. num-supernodes moved from the app's pyproject.toml to ~/.flwr/config.toml (the SuperLink connection config), so participant-count capture from pyproject returns only run-config-derived hints. Reading the active Flower config is a planned enhancement.
• Dataset identifiers, ethics/governance lineage are not reachable from Flower and must be supplied by the user (e.g. via config) — relevant for regulated domains.
• Per-client metadata is out of scope by design. Flower exposes only per-round aggregates, which aligns with FL's privacy premise.

Changelog

• 0.4.0 — renamed to flwrCrate (dist/import flwrcrate); config key is now [tool.flwrcrate.metric-uris] (the legacy [tool.fedacrate.*] key is still read).
• 0.3.0 — run discoverable from the crate root (mentions); aggregation strategy emitted as a SoftwareApplication with hyperparameters; per-round metrics + federation details in metrics_log.json; all declared dependencies captured (deny-list instead of allow-list); author/license/agent provenance scaffolding.
• 0.2.0 — initial working version.

Development

pip install -e ".[dev]"   # installs pytest
pytest                    # run the full unit + integration suite

The suite is split into fast unit tests (dependency parsing, metric handling, slug/person helpers, crate assembly) and integration tests that drive the whole FLCrateTracker lifecycle and assert a complete, correct ro-crate-metadata.json — without needing Flower, Ray, or any ML framework installed. CI runs it on Python 3.10–3.12 (see the badge above).

License

MIT

Acknowledgements

Developed within the ELIXIR Fed-A-Crate project (WP6) toward milestone M6.7, building on the Federated Learning RO-Crate profile by the eScienceLab (The University of Manchester).