Federated Learning is training models across devices without centralizing data. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0124, within the Risk family. The core principle: training models across devices without centralizing data. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
Scientific Definition
Training models across devices without centralizing data.
Plain-English Definition
Training models across devices without centralizing data.
Feynman Explanation
The model travels. The data stays home.
Core Principle
Training models across devices without centralizing data.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
The model travels. The data stays home.
Examples
- Mobile keyboard prediction training.
- Privacy-by-design pattern.
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Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
When this element shows up in a diagnostic, the instinct is to train people out of it. Training rarely moves it. The mechanism underneath it operates in the Incentives dimension — what makes behavior more or less likely?. You can recognize it in the field by its signature: the model travels. The data stays home. Every element in the Incentives dimension changes the perceived payoff of an action before the action happens, which is exactly where incentive design has leverage.
How it gets exploited
Left undesigned, privacy-by-design pattern. It is amplified whenever privacy-by-design pattern. Inside organizations that shows up as privacy-by-design pattern. The pattern is the same one Goodhart's Law describes: the measurable proxy attracts the effort, and the purpose behind it quietly loses funding.
How the Lab designs around it
The redesign move is to consider when data centralization is the bottleneck or risk. Design against it the way you would design against a known failure mode — assume it will appear, and price the exploit before someone finds it.
Famous Experiments
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Design Principles
- Consider when data centralization is the bottleneck or risk.
Measurement Approaches
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Evidence
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The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Consider when data centralization is the bottleneck or risk.
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Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
When the agent acts, who's responsible?
Categorizing AI use cases by risk level.
Systematic skew in model behavior across groups.
Testing model behavior on hypothetical alternate inputs.
Adding noise to data to protect individual privacy.
Quantitative measures of model behavior across groups.
Bypassing model safety constraints.
Individual speed gains hide collective quality decline.
Malicious instructions hidden in user input or retrieved content.
Adversarial testing of AI systems.
Foundational skills erode through AI offloading.
Models learning from examples in the prompt.
Where Federated Learning is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- Field guideIncentives: definition, types, examples
The parent field guide for this element.
- ReferenceThe laws of incentives
Goodhart, Campbell, and the Cobra Effect.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- EssayIncentives Under Crisis
How this element behaves under pressure.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
Questions about Federated Learning
- What is Federated Learning?
- Federated Learning is training models across devices without centralizing data. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0124, within the Risk family. The core principle: training models across devices without centralizing data. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
- What is an example of Federated Learning?
- Privacy-by-design pattern. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0124).
- How is Federated Learning exploited?
- Privacy-by-design pattern.
- How do you design around Federated Learning?
- Consider when data centralization is the bottleneck or risk.
- Which behavioral dimension does Federated Learning belong to?
- Federated Learning is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Risk", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0124 and its evidence grade is C.