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AI Glossary

Federated Learning

A machine learning approach where models are trained across multiple devices without sharing raw data.

TL;DR

  • —A machine learning approach where models are trained across multiple devices without sharing raw data.
  • —Understanding Federated Learning is critical for effective AI for companies.
  • —Remova helps companies implement this technology safely.

In Depth

Federated learning allows AI models to learn from distributed data sources without centralizing the data. This preserves privacy and data sovereignty while still benefiting from diverse datasets. It's particularly relevant for healthcare, finance, and cross-border collaborations.

Related Terms

On-Premises AI

AI deployment where all infrastructure, models, and data processing occur within an organization's own facilities.

Data Sovereignty

The principle that data is subject to the laws and governance of the jurisdiction where it is collected or processed.

Training Data

The dataset used to train an AI model, which significantly influences its capabilities and biases.

AI Ethics

The principles and guidelines governing the responsible development and use of AI systems.

Knowledge Hub

Glossary FAQs

Federated Learning is a fundamental concept in the AI for companies landscape because it directly impacts how organizations manage a machine learning approach where models are trained across multiple devices without sharing raw data.. Understanding this is crucial for maintaining AI security and compliance.
Remova's platform is built to natively manage and optimize Federated Learning through our integrated governance layer, ensuring that your organization benefits from this technology while mitigating its inherent risks.
You can explore our full AI for companies glossary, which includes detailed definitions for related concepts like On-Premises AI and Data Sovereignty.

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