Federated Learning
A training approach in which a model is trained across many separate devices or organizations without their raw data ever being centralized, introduced in its modern form by McMahan et al. (Google, 2017). Federated training does not remove the row-level unlearning problem; it adds a coordination challenge to it, since removing one participant's influence may depend on that participant's continued cooperation.
Defined in 4 GAGE programs, which carry 6 distinct definitions of it. The wording above is taught in Certified AI Data Governance Professional (CADGP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A training approach in which a model is trained across many separate devices or organizations without their raw data ever being centralized, introduced in its modern form by McMahan et al. (Google, 2017). Federated training does not remove the row-level unlearning problem; it adds a coordination challenge to it, since removing one participant's influence may depend on that participant's continued cooperation.
A model-training technique in which each device (each robot in a fleet) computes a local model update from its own data, and only that update, not the underlying raw data, is sent to a central server and combined across the fleet, allowing a shared model to improve without raw sensor data ever leaving individual devices.
Training a model across devices or institutions without centralising raw data, moving model updates instead. Reduces data movement; not by itself anonymisation.
Training where the model goes to the data on each device and only model updates return, keeping raw data local.
A model-training architecture in which a shared model is trained across multiple organizations' or devices' data without the raw data ever leaving its original source; reduces certain centralization and transfer risks but does not by itself resolve either the memorization test or the provenance test for the resulting model.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Privacy and Data Rights in AI Systems · Ethical and Responsible AI and Operational Governance, Certified AI Practitioner: Workplace Foundations
- Synthetic data: when it protects, when it launders, and how to tell · Feeding the Machines, Certified AI Data Governance Professional (CADGP)
- Data poisoning: how an attacker teaches your model on purpose · Poison, Leaks, and the Adversary, Certified AI Data Governance Professional (CADGP)
- Machine unlearning: the emerging answer to deleting what a model learned · The Frontier Discipline, Certified AI Data Governance Professional (CADGP)
- Privacy incidents, safeguards, and PETs · Privacy and Data Governance, Certified Enterprise Governance, Risk and Compliance Professional (CEGP)
- Privacy when a robot's sensors record real spaces · Responsible Physical AI in Practice, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Federated Learning starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Builds minimization, purpose limitation, de-identification, consent and retention into an AI system before launch, with tests that prove it.
3 lessons on this page teach it as a primary outcome.
Applies GDPR, CCPA and sector rules to training data, inference, automated decisions, lawful basis, individual rights and cross-border transfer.
2 lessons on this page teach it as a primary outcome.
Keeps the living record: each risk with a named owner, rating, treatment, residual risk, monitoring metric, threshold and review date.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- Chief Privacy Officer, AI privacy editionneeds it at depth
- AI Privacy Engineerneeds it at depth · 6 open postings this week
- Privacy Analystneeds working knowledge
- AI Regulatory Counselneeds it at depth · 14 open postings this week
- AI Risk Managerneeds it at depth · 2 open postings this week
- Chief Data Officer, data leadership editionneeds working knowledge
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: ISO/IEC 27701; GDPR Article 25; GDPR; CCPA/CPRA.
One interview question about it
How do you build privacy into an AI product from the design stage rather than checking it at the end?
Asked of candidates for privacy by design and privacy engineering. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Privacy and data protection carries it as work you can show: Privacy assessments and lawful-basis records are your work product.
- Legal and paralegal work carries it as a claim to back up: Privacy and consumer law are usually within reach.
- A law degree (JD, LLB or LLM), no AI work yet carries it as a claim to back up: Privacy and data protection law are usually a course, not yet a file.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.