Federated Learning
Quick Answer
Federated learning is an approach to training machine learning models across multiple decentralised devices or servers, without moving the underlying data to a central location. Only model updates are exchanged, preserving privacy and data sovereignty.
In Depth
What Federated Learning really means
Federated learning is attractive wherever data is sensitive or legally restricted: across hospitals sharing imaging models, banks collaborating on fraud detection, or mobile devices improving on-device keyboards.
Challenges include non-identically distributed data across participants, communication overhead, and the need for robust protocols to prevent information leakage through model updates themselves.
Why It Matters
Business relevance for UK organisations
UK healthcare, financial services and research organisations increasingly explore federated learning to collaborate on model quality without breaching data-protection obligations.
Real-world example
How this shows up in practice
A consortium of UK NHS trusts used federated learning to train a radiology triage model across hospitals, without any patient images leaving individual hospital networks.
Related Terms
Continue exploring
Machine Learning
Machine Learning (ML) is a subfield of AI in which systems learn patterns from historical data rather than following explicitly programmed rules, enabling them to make predictions or decisions on new, unseen data as conditions evolve.
AdvancedMLOps
MLOps is the discipline of operationalising machine learning: the practices, tools and culture needed to deploy, monitor, retrain and govern models reliably in production. It extends DevOps thinking to the unique challenges of data and models.
BusinessAI Governance
AI governance is the set of policies, roles, controls and oversight mechanisms that ensure AI is used responsibly, safely and in line with law and organisational values. Effective governance is proportionate — tight where risk is high, light where risk is low.
BasicsTraining Data
Training data is the dataset used to teach a machine learning model the patterns it needs to perform its task. The quality, quantity, diversity and recency of training data directly determine how accurate and fair the resulting model will be.