Federated Learning
Definition
A machine learning technique that trains models across multiple decentralised devices or servers holding local data, without transferring the raw data to a central location. Federated learning addresses data privacy and sovereignty concerns by keeping sensitive data on-device while still enabling collaborative model improvement. It is particularly relevant in healthcare, finance, and telecommunications where data sharing restrictions are stringent.
Complementary Terms
Concepts that frequently appear alongside Federated Learning in practice.
A mathematical model trained on data to identify patterns and make predictions without being explicitly programmed for each task. Machine learning models underpin many AI-driven business applications, from demand forecasting to fraud detection, and their development costs are increasingly recognised as intangible assets under IAS 38 when they meet the identifiability and future economic benefit criteria.
A machine learning technique where a model trained on one task is repurposed as the starting point for a different but related task, significantly reducing the data and compute required for training. Transfer learning accelerates AI development timelines and reduces costs, making AI adoption more accessible to SMEs.
Artificially generated data that mimics the statistical properties of real-world datasets, used to train machine learning models when actual data is scarce, sensitive, or expensive to obtain. Synthetic data enables AI development in privacy-constrained domains such as healthcare and finance, while reducing data acquisition costs and regulatory exposure.
A secure, privacy-preserving technology environment that enables multiple parties to combine and analyse their datasets without either party gaining access to the other's raw data. Data clean rooms use cryptographic techniques, aggregation rules, and access controls to enable collaborative analytics while maintaining data privacy compliance.
The principle that data is subject to the laws and governance structures of the country in which it is collected or stored. Data sovereignty requirements affect cloud computing architecture, cross-border data transfers, and vendor selection, particularly in light of GDPR restrictions on transfers to countries without adequate data protection standards.
A set of practices combining machine learning, DevOps, and data engineering to standardise and streamline the end-to-end lifecycle of machine learning models, from development through deployment to monitoring. MLOps encompasses version control for models and data, automated testing, continuous integration and deployment, and model performance monitoring in production.
A self-executing program stored on a blockchain that automatically enforces the terms of an agreement when predetermined conditions are met, without requiring intermediaries. Smart contracts enable trustless transactions, automated escrow, decentralised finance protocols, and programmable business logic.
Data collected by entities that do not have a direct relationship with the individuals whose data is being gathered, typically aggregated from multiple sources and sold to other organisations for marketing, analytics, or enrichment purposes. The value and availability of third-party data have declined sharply due to privacy regulations (GDPR, CCPA), browser restrictions on third-party cookies, and growing consumer demand for data transparency.
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