AI Governance Engineer
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Federated learning

By Updated

Training a model across devices or sites where the data lives, sharing model updates instead of raw records 1. It limits data movement but does not by itself hide personal data, because shared updates can leak training examples.

Developed in
ch. 19, Privacy-enhancing technologies and their honest limits
Chapters
ch. 19, Privacy & AI
Source
1 numbered reference, listed below

Where it is used

One chapter of the Body of Knowledge uses the term. Each link opens the first section that does.

  • Differential privacy

    A mathematical guarantee that bounds how much any single person's record can change the output of an analysis or a trained model, tuned by a privacy budget.

  • Privacy-enhancing technology (PET)

    A technique that reduces what an attacker, vendor or insider can learn from personal data, such as differential privacy, federated learning, synthetic data…

Sources

  1. [1] "Communication-Efficient Learning of Deep Networks from Decentralized Data" (McMahan et al.; federated learning; arXiv 1602.05629). arXiv. 2016-02-17. https://arxiv.org/abs/1602.05629 (verified: primary)

Definitions of legal terms paraphrase the cited text, which governs. Dated statements are as of .

Cite this term

García Aibar, J. (2026). Federated learning. In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0), Glossary. https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/glossary/federated-learning. CC BY 4.0

BibTeX

@misc{aige2026federatedlearning,
  author  = {Jorge García Aibar},
  title   = {{Federated learning}},
  note    = {Glossary, AI Governance Engineering: The Thesis \& Body of Knowledge, version 0.5.0},
  year    = {2026},
  doi     = {10.5281/zenodo.22956197},
  url     = {https://aigovernanceengineer.com/glossary/federated-learning}
}