[200] SERVERAPP_STRATEGY_PRECONDITION_UNMET
===========================================

.. |arrayrecord_link| replace:: ``ArrayRecord``

.. |fedavg_link| replace:: ``FedAvg``

.. |metricrecord_link| replace:: ``MetricRecord``

.. |recorddict_link| replace:: ``RecordDict``

.. _arrayrecord_link: ref-api/flwr.common.ArrayRecord.html

.. _fedavg_link: ref-api/flwr.serverapp.FedAvg.html

.. _metricrecord_link: ref-api/flwr.common.MetricRecord.html

.. _recorddict_link: ref-api/flwr.common.RecordDict.html

Description
-----------

The strategy received replies that cannot be aggregated because either not all replies
contain a supported number of records, because the keys used within each record do not
match those in other replies, or because the key required by the strategy to perform
weighted averaging is missing.

How to Resolve
--------------

Please ensure all replies returned by ClientApps have one |arrayrecord_link|_ (none when
replies are from a round of federated evaluation, i.e. when message type is
`MessageType.EVALUATE`) and one |metricrecord_link|_.

The records in all replies must use identical keys. In addition, if the strategy expects
a key to perform weighted average (e.g. in |fedavg_link|_) please ensure the returned
|metricrecord_link|_ from ClientApps do include this key.

Let's see a few examples of payloads (|recorddict_link|_) that can and can't be
aggregated. Let's assume the `ServerApp` has been configured to use |fedavg_link|_ with
`num-examples` as the key to perform weighted averaging:

These two payloads can be aggregated because all keys match, there is one record of each
type, and the key for weighted averaging is present.

.. code-block:: python

    payload1 = RecordDict(
        {
            "model-parameters": ArrayRecord(...),  # no ArrayRecord if in an evaluate round
            "metrics": MetricRecord({"loss": 0.123, "num-examples": 1234}),
        }
    )
    payload2 = RecordDict(
        {
            "model-parameters": ArrayRecord(...),  # no ArrayRecord if in an evaluate round
            "metrics": MetricRecord({"loss": 0.01, "num-examples": 1234}),
        }
    )

These payloads can't be aggregated because they have unmatching keys (see one has
`loss`, the other has `loss` and `accuracy`)

.. code-block:: python

    payload1 = RecordDict(
        {
            "model-parameters": ArrayRecord(...),
            "metrics": MetricRecord({"loss": 0.123, "num-examples": 1234}),
        }
    )
    payload2 = RecordDict(
        {
            "model-parameters": ArrayRecord(...),
            "metrics": MetricRecord({"loss": 0.01, "accuracy": 0.99, "num-examples": 1234}),
        }
    )

These two payloads can't be aggregated (one has a missing `ArrayRecord`)

.. code-block:: python

    payload1 = RecordDict(
        {
            "model-parameters": ArrayRecord(...),
            "metrics": MetricRecord({"loss": 0.123, "num-examples": 1234}),
        }
    )
    payload2 = RecordDict({"metrics": MetricRecord({"loss": 0.01, "num-examples": 1234})})

These payloads can't be aggregated because there is no key for weighted averaging

.. code-block:: python

    payload1 = RecordDict(
        {
            "model-parameters": ArrayRecord(...),
            "metrics": MetricRecord({"loss": 0.123, "accuracy": 0.83}),
        }
    )
    payload2 = RecordDict(
        {
            "model-parameters": ArrayRecord(...),
            "metrics": MetricRecord({"loss": 0.01, "accuracy": 0.99}),
        }
    )