AuraOne / Products
Loading capabilities, workflow, and program details.
Models / Federated Learning
Follow participant eligibility, aggregation policy, privacy checks, exceptions, and the governed model handoff.
Model and data teams operating distributed training across controlled participants. Each program is configured around the workflow, review policy, and evidence your team needs.
Input
Work
Output
Move from intake to review and handoff with clear owners at every step.
01
Participant policies, local datasets, model version, and privacy rules.
Participant attestations
02
Validate participants, train locally, aggregate, review, and attest.
Aggregation record
03
Eligibility, aggregation integrity, privacy bounds, and drift. Training owner, privacy reviewer, and model approver.
Named review and decision history
04
A governed model update with participant and privacy evidence.
Privacy review
What you receive
| Outcome | Work | What you receive | Program fit |
|---|---|---|---|
| Advance | The required Federated Learning checks and review are complete. | Participant attestations, Aggregation record, Privacy review | Program fit depends on the source, version, criteria, reviewers, and operating scope. |
| Hold | A required check, reviewer, approval, or source record is missing or unresolved. | Blocking requirement, responsible owner, source record, and required recovery. | The responsible owner and recovery step stay visible until the issue is resolved. |
| Return for work | The source object or workflow requires correction and another review. | Returned items, responsible owner, expected evidence, and review route. | The updated work returns through the same review path before a new decision. |
Inspect the workflow
