Project: federated depot model release review

Last updated: 5 Oct 20265 min read

project

A federated release review joins label compatibility, accepted-client coverage, site-level performance, update controls and rollback into one decision packet.

Freeze the round design

Three repair depots train a 37-day follow-up predictor. Before the run, freeze label maturity, feature units, crop version, starting checkpoint, eligible-client list, local epoch budget and update rejection rules. Keep later-period local test sets untouched until round selection ends. The client contract identifies the fields that must agree.

Compare sensible alternatives

Evaluate a local-only baseline at each depot, a global model trained with example-weighted updates, and an equal-site weighting candidate. Use the same local holdouts and action threshold for the comparison. Record communication rounds and total upload bytes as well as error costs. Weighted aggregation specifies the numerical choice.

Review who contributed

Count eligible, invited, finished, accepted and stale clients by round and site type. If one remote depot rarely participates, its eventual test result still matters. Inspect update disagreement and local-step sensitivity without treating either as proof of causality. Dropouts and client drift need separate readings.

Evaluate the target population

Report micro and macro performance, the worst site, rare-failure recall with mature-positive counts, and calibration by depot. A global gain that fails a small depot’s minimum standard is a hold. The code below applies explicit gates to a hypothetical packet; it does not train a model or establish a privacy guarantee. Site denominators belong beside every result.

Check the security and rollback path

Verify update authentication, allowed metadata, server retention, threat model, compatible client versions and rollback to the previous checkpoint. A shadow run can test logging before decisions use the new model. Update exposure remains relevant even with no raw image transfer.

Implementation

release_packet = {
    "contract_versions_match": True,
    "accepted_client_fraction": 0.75,
    "micro_accuracy_gain": 0.024,
    "worst_site_recall": 0.58,
    "minimum_site_recall": 0.64,
    "rare_event_support_checked": True,
    "update_security_reviewed": True,
    "rollback_checkpoint_kept": True,
}

def federated_release_gate(packet):
    holds = []
    if not packet["contract_versions_match"] or packet["accepted_client_fraction"] < 0.70:
        holds.append("client contract or coverage")
    if packet["micro_accuracy_gain"] <= 0 or packet["worst_site_recall"] < packet["minimum_site_recall"]:
        holds.append("site performance")
    if not all(packet[key] for key in (
        "rare_event_support_checked", "update_security_reviewed", "rollback_checkpoint_kept"
    )):
        holds.append("release controls")
    return "hold: " + ", ".join(holds) if holds else "eligible for shadow evaluation"

assert federated_release_gate(release_packet) == "hold: site performance"

Performance and operating cost

The gate is O(1). Producing its evidence requires repeated local training, communication rounds, mature holdouts, site-level evaluation and an update security review. The numerical thresholds are example deployment rules, not universal defaults. Preserve the previous model and preprocessing versions until a rollback drill passes.

Common Mistakes

Do not promote from a pooled gain when one depot fails its minimum.
Do not claim private training from local data residence alone.
Do not ignore nonparticipating sites or stale update packets.