Federated Learning
What is Federated Learning?
A machine learning approach where a model is trained across multiple decentralised devices or servers holding local data samples, without exchanging the raw data. Federated learning helps preserve data privacy while enabling collaborative model training.
Terms that appear alongside federated learning
Each of these is named in at least one of the same controls as federated learning. The number is how many controls name both.
- differential privacy 6 shared controls
- gdpr 5 shared controls
- encryption 3 shared controls
- pseudonymisation 3 shared controls
- nist 3 shared controls
- compliance 3 shared controls
- audit 3 shared controls
- data minimisation 3 shared controls
Frameworks that govern federated learning
What the standards actually require on federated learning
Requirements naming federated learning across 4 standards, quoted from the control text.
Security Dimension 8 Privacy per X.805 Clause 6.8: Privacy shields what could be inferred by watching activity on the network, for example websites visited by users + their geographic location + the IP addresses and DNS names assigned to devices inside a provi...
X805-Dim8-Privacy-Identification-Network-Activity-Personal-Information-Confidentiality · ITU-T X.805 Security Dimension 8 - Privacy + Identification of Network Activity + Personal Information Confidentiality + Subscriber Anonymity + Pseudonymity + Anti-Tracking + Location Privacy + Data Minimization + GDPR/CCPA Alignment →Section 34 of the Jamaica Data Protection Act 2020 establishes Privacy by Design + Privacy by Default + and Data Protection Impact Assessment (DPIA) requirements.
JM-DPA2020-Privacy-by-Design-Default-Sec34-Engineering-Data-Protection-Impact-Assessment-DPIA-Risk-Based · Jamaica DPA 2020 Privacy by Design + Privacy by Default + Section 34 + Data Protection Impact Assessment (DPIA) + Risk-Based + High-Risk Processing + Prior Consultation + Privacy Engineering →Section 7 of KOSA establishes a framework for qualified researcher access to covered platform data for public interest research. (1) Section 7(a) Qualified Researcher Definition: (a) Affiliated with accredited higher education institution + non-profit research...
KOSA-Researcher-Access-Section7-Qualified-Researchers-Public-Interest-Research-Approval-Process-Data-Sharing-Protections · KOSA Researcher Access + Section 7 + Qualified Researchers + Public Interest Research + Approval Process + Data Sharing + Privacy Protections + Methodology Standards + Public Reporting + Academic + Civil Society + Government Researchers →Review data quality for incorrect labels and representativeness; apply privacy-preserving and responsible data governance (data cleaning, on-device processing, federated learning) and monitor pre-trained models used;
oecd-due-diligence-guidance-for-responsible-ai-2026::3.1.G2-data · 3.1.G2-data Mitigation by developers: responsible data sourcing and training →Questions people ask about federated learning
What is Federated Learning?
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See how Federated Learning applies across compliance frameworks
Our platform maps 1,016 frameworks with 316K cross-framework control mappings. Explore how this concept is addressed across standards.